Applied Science, Faculty of
Mechanical Engineering, Department of
DSpace
UBCV
Sakr, Maram
Doctor of Philosophy - PhD
University of British Columbia
Robots have the potential to support humans in diverse domains such as industrial automation, healthcare, and household tasks. However, enabling robots to learn new tasks remains challenging, especially for non-expert users. Learning from Demonstration (LfD) offers a promising solution by allowing robots to learn directly from human demonstrations rather than requiring explicit programming. Despite its potential, the success of LfD hinges on the quality, quantity, and diversity of the demonstrations provided. This thesis addresses the challenges of ensuring high-quality and informative demonstrations to improve learning and generalization. First, it introduces a performance-based framework to evaluate demonstration quality, showing that robot task success and generalization can serve as objective measures. It also proposes a set of consistency-based metrics that can be applied prior to training, enabling the identification and filtering of low-quality demonstrations in advance, thus improving learning outcomes. To further enhance demonstration quality, this thesis explores train-the-trainer methodologies that help novice users without a robotics or programming background provide expert-level demonstrations. Three different training strategies are investigated: discovery training, where users learn through practice and self-reflection; observational training, where users watch expert demonstrations; and kinesthetic training, where users experience expert movements through physical interaction with the robot. User studies validate the effectiveness of these approaches, showing improvements in demonstration quality, and the ability to transfer skills to new tasks. Finally, this thesis explores the informativeness of demonstrations, addressing the issue of data sparsity in LfD. Instead of treating all demonstrations equally, we propose using information entropy to identify the most uncertain regions in the task space, prompting users to provide additional demonstrations where they are most needed. This approach improves learning efficiency by reducing redundant demonstrations while ensuring sufficient data coverage for generalization. Across multiple experiments and user studies, the findings demonstrate that combining quality assessment, targeted user training, and data-efficient selection strategies significantly enhances robot learning performance. This work advances LfD by making it more adaptive, scalable, and accessible to non-experts. Ultimately, it contributes to the development of intuitive human-robot interaction systems where everyday users can teach robots effectively, opening new possibilities for deploying robots in real-world, user-centered environments.
Democratizing Robot LearningTeaching by Demonstration with Everyday Users inMindbyMaram SakrB.Sc., Computer and Systems Engineering, Mansoura University, 2012M.Sc., Engineering Science, Simon Fraser University, 2017A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Mechanical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)October 2025© Maram Sakr 2025The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:Democratizing Robot Learning: Teaching by Demonstrationwith Everyday Users in MindSubmitted by Maram Sakr in partial fulfillment of the requirementsfor the degree of Doctor of Philosophy in Mechanical Engineering.Examining Committee:Elizabeth Croft, Professor, Mechanical Engineering and Electrical and Com-puter Engineering, University of Victoria.Co-supervisorDana Kulic´, Professor, Electrical and Computer Systems Engineering, MonashUniversity.Co-supervisorDominic Liao-McPherson, Assistant Professor, Mechanical Engineering, UBC.Co-supervisorMike Van der Loos, Associate Professor Emeritus, Mechanical Engineering,UBC.Supervisory Committee MemberRobert Rohling, Professor, Mechanical Engineering and Electrical and Com-puter Engineering, UBC.University ExaminerChao Chen, Associate Professor, Mechanical and Aerospace Engineering,Monash University.University ExaminerKerstin Dautenhahn, Professor, Electrical and Computer Engineering, Uni-versity of Waterloo.External ExaminerWafa Johal, Associate Professor, School of Computing & Information Sys-tems, University of Melbourne.External ExamineriiAbstractRobots have the potential to support humans in diverse domains such asindustrial automation, healthcare, and household tasks. However, enablingrobots to learn new tasks remains challenging, especially for non-expertusers. Learning from Demonstration (LfD) offers a promising solution byallowing robots to learn directly from human demonstrations rather thanrequiring explicit programming. Despite its potential, the success of LfDhinges on the quality, quantity, and diversity of the demonstrations pro-vided. This thesis addresses the challenges of ensuring high-quality andinformative demonstrations to improve learning and generalization.First, it introduces a performance-based framework to evaluate demon-stration quality, showing that robot task success and generalization can serveas objective measures. It also proposes a set of consistency-based metricsthat can be applied prior to training, enabling the identification and fil-tering of low-quality demonstrations in advance, thus improving learningoutcomes.To further enhance demonstration quality, this thesis explores train-the-trainer methodologies that help novice users without a robotics or program-ming background provide expert-level demonstrations. Three different train-ing strategies are investigated: discovery training, where users learn throughpractice and self-reflection; observational training, where users watch ex-pert demonstrations; and kinesthetic training, where users experience ex-pert movements through physical interaction with the robot. User stud-ies validate the effectiveness of these approaches, showing improvements indemonstration quality, and the ability to transfer skills to new tasks.Finally, this thesis explores the informativeness of demonstrations, ad-dressing the issue of data sparsity in LfD. Instead of treating all demon-strations equally, we propose using information entropy to identify the mostuncertain regions in the task space, prompting users to provide additionaldemonstrations where they are most needed. This approach improves learn-ing efficiency by reducing redundant demonstrations while ensuring sufficientdata coverage for generalization.Across multiple experiments and user studies, the findings demonstrateiiAbstractthat combining quality assessment, targeted user training, and data-efficientselection strategies significantly enhances robot learning performance. Thiswork advances LfD by making it more adaptive, scalable, and accessibleto non-experts. Ultimately, it contributes to the development of intuitivehuman-robot interaction systems where everyday users can teach robotseffectively, opening new possibilities for deploying robots in real-world, user-centered environments.iiiLay SummaryTeaching robots new tasks is challenging, especially for people without pro-gramming experience. Learning from Demonstration (LfD) allows robots tolearn by imitating human demonstrations, but poor-quality demonstrationscan lead to poor robot performance. This thesis explores methods to im-prove demonstration quality, train novice users, and optimize data collectionfor better robot learning.First, it introduces an approach to measure demonstration quality, show-ing that higher-quality demonstrations improve robot performance. Then,it examines training methods to help users provide better demonstrations,such as observing experts or physically interacting with the robot. Finally,it explores how to select the most informative demonstrations, reducing theamount of data needed for learning. By combining quality assessment, usertraining, and efficient data selection, this research makes robot learning eas-ier, faster, and more accessible. These findings contribute to a future whereanyone can teach robots new skills effortlessly, improving their usefulness indaily life.ivPrefaceResearch presented in this thesis was conducted at the Collaborative Ad-vanced Robotics and Intelligent Systems (CARIS) Laboratory at the Univer-sity of British Columbia, Vancouver, Canada. Part of the work presentedin Chapter 3 and Chapter 4 was conducted at the Robotics Laboratoryat Monash University, Melbourne, Australia. The user studies included inthis thesis were approved by the UBC Research Ethics Board (identificationnumbers H20-03740 and H20-03740-A001). The user study in Chapter 3and Chapter 4 was approved by the Monash University Human Ethics Com-mittee (Project ID: 37303). Work in this thesis has been reported in thefollowing publications:â¢ A modified version of Chapter 2 appeared in:Maram Sakr, Zexi Jesse Li, H.F. Machiel Van der Loos, Dana Kulic´,and Elizabeth Croft. âQuantifying Demonstration Quality for RobotLearning and Generalizationâ, IEEE Robotics and Automation Let-ters, 7(4), pp. 9659-9666, 2022.My contributions to this paper include conceptualizing the projectâsidea and scope, conducting the literature review, implementing thesystem, designing and conducting the evaluation experiments and userstudy, analyzing the results, and writing the paper. I was the primarysupervisor for Zexi Jesse Li, who contributed to several aspects ofthe implementation. H.F. Machiel Van der Loos, Dana Kulic´, andElizabeth Croft supervised the project and contributed to editing andreviewing the paper.â¢ A modified version of Chapter 3 appeared in:Maram Sakr, Juyan Zhang, H.F. Machiel Van der Loos, Dana Kulic´,and Elizabeth Croft. âConsistency Matters: Defining DemonstrationData Quality Metrics in Robot Learning from Demonstrationâ, ACMTransactions on Human-Robot Interaction, 2024 (in press).andvPrefaceMaram Sakr, H.F. Machiel Van der Loos, Dana Kulic´, and ElizabethCroft. âWhat Makes a Good Demonstration for Robot Learning Gen-eralization?â, Companion of the ACM/IEEE International Conferenceon Human-Robot Interaction (HRI), HRI Pioneers Workshop, March2021. (Acceptance rate: â¼ 24%).My contributions to these papers include conceptualizing the projectâsidea and scope, conducting the literature review, implementing the sys-tem, designing and conducting the evaluation experiments and userstudy, analyzing the results, and writing the paper. Juyan Zhang atMonash University contributed to some aspects of implementations.H.F. Machiel Van der Loos, Dana Kulic´, and Elizabeth Croft super-vised the project and contributed to editing and reviewing the paper.â¢ A modified version of Chapter 4 appeared in:Maram Sakr, Martin Freeman, H.F. Machiel Van der Loos, and Eliz-abeth Croft. âTraining Human Teachers to Improve Robot Learningfrom Demonstration: A Pilot Study on Kinesthetic Teachingâ, IEEEInternational Conference on Robot and Human Interactive Communi-cation (RO-MAN), 2020.andMaram Sakr, Alexander Calvert, Rachel Love, H.F. Machiel Vander Loos, Dana Kulic´, and Elizabeth Croft. âEmpowering Lay Users:Evaluating Training Techniques to Improve Robot Teaching Skillsâ, tobe submitted to the ACM Transactions on Human-Robot Interaction.My contributions to these papers include conceptualizing the projectâsidea and scope, conducting the literature review, implementing the sys-tem, designing and conducting the evaluation experiments and userstudy, analyzing the results, and writing the paper. I was the pri-mary supervisor for Martin Freeman at UBC and Alexander Calvertat Monash University, both of whom contributed to various aspects ofthe implementation. My colleague Rachel Love assisted in conductingthe user study. H.F. Machiel Van der Loos, Dana Kulic´, and ElizabethCroft supervised the project and contributed to editing and reviewingthe paper.â¢ A modified version of Chapter 5 appeared in:Maram Sakr, Logan Zhang, Benjamin Li, Haomiao Zhang, H.F.Machiel Van der Loos, Dana Kulic´, and Elizabeth Croft. âHow CanviPrefaceEveryday Users Teach Robots Efficiently from Demonstrations?â, ACMTransactions on Human-Robot Interaction, 14(4), pp. 1-22, 2025.My contributions to this paper include conceptualizing the projectâsidea and scope, conducting the literature review, implementing thesystem, designing and conducting the evaluation experiments and userstudy, analyzing the results, and writing the paper. I was the primarysupervisor for Logan Zhang and Benjamin Li, who contributed to sev-eral aspects of the implementation. My colleague Haomiao Zhang as-sisted in conducting the user study. H.F. Machiel Van der Loos, DanaKulic´, and Elizabeth Croft supervised the project and contributed toediting and reviewing the paper.The use of Generative AI in this thesis was limited to the use of ChatGPTfor proofreading purposes. No content, ideas, or analyses were generatedby the tool. All research questions, methodologies, interpretations, andconclusions are entirely my own and reflect my independent academic work.This use was approved by my supervisors and was solely for improving thereadability of the writing.viiTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . xixDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2.1 Robot Learning from Demonstrations . . . . . . . . . 31.2.2 Teaching Interfaces . . . . . . . . . . . . . . . . . . . 41.2.3 Human Teachers in LfD . . . . . . . . . . . . . . . . . 51.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.4 Thesis Objectives . . . . . . . . . . . . . . . . . . . . . . . . 81.5 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . 92 Quantifying Demonstration Quality for Robot Learning andGeneralization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 122.3 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . 132.4 Experimental Design . . . . . . . . . . . . . . . . . . . . . . 142.4.1 Robot Platform . . . . . . . . . . . . . . . . . . . . . 14viiiTable of Contents2.4.2 Task Definition . . . . . . . . . . . . . . . . . . . . . 142.4.3 User Study . . . . . . . . . . . . . . . . . . . . . . . . 152.5 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.5.1 Learning and Generalization Evaluation . . . . . . . . 182.5.2 User Adaptation . . . . . . . . . . . . . . . . . . . . . 182.6 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.7 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.7.1 Task Performance Analysis . . . . . . . . . . . . . . . 212.7.2 Task Generalization Performance Analysis . . . . . . 232.7.3 Mixed Quality Demonstrations Analysis . . . . . . . 242.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.9 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . 283 Consistency Matters: Defining Demonstration Data QualityMetrics in Robot Learning from Demonstration . . . . . . 303.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 313.2.1 Learning from Mixed-Quality Demonstrations . . . . 313.2.2 Metrics for Evaluating Demonstration Quality . . . . 323.3 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . 343.4 Experimental Design . . . . . . . . . . . . . . . . . . . . . . 353.4.1 Experiment 1: Button Pressing . . . . . . . . . . . . 363.4.2 Experiment 2: Pick and Place . . . . . . . . . . . . . 373.4.3 Evaluation of Learning and Generalization Performance413.5 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433.6.1 Consistency impact on the task and generalized suc-cess rate . . . . . . . . . . . . . . . . . . . . . . . . . 443.6.2 Impact of practice on demonstration consistency . . . 453.6.3 Practice impact on the task and generalized successrate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493.6.4 The importance of each metric to the task success rate 503.6.5 The importance of each metric to the generalized suc-cess rate . . . . . . . . . . . . . . . . . . . . . . . . . 543.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543.8 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . 57ixTable of Contents4 Empowering Lay Users: Evaluating Training Techniques toImprove Robot Teaching Skills . . . . . . . . . . . . . . . . . 594.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 594.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 604.2.1 Human Teaching Behavior in Robot Learning . . . . 604.2.2 Teaching Guidance and User Training . . . . . . . . . 614.3 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . 624.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . 634.4.1 Experimental Tasks . . . . . . . . . . . . . . . . . . . 644.4.2 User Study Procedure . . . . . . . . . . . . . . . . . . 654.4.3 Participants . . . . . . . . . . . . . . . . . . . . . . . 674.5 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674.5.1 Objective Measures . . . . . . . . . . . . . . . . . . . 684.5.2 Subjective Measures . . . . . . . . . . . . . . . . . . . 684.6 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694.7 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704.7.1 Objective Measures . . . . . . . . . . . . . . . . . . . 704.7.2 Subjective Measures . . . . . . . . . . . . . . . . . . . 754.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784.9 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . 805 How Can Everyday Users Efficiently Teach Robots by Demon-stration? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 825.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 835.2.1 Active Learning . . . . . . . . . . . . . . . . . . . . . 845.2.2 Machine Teaching . . . . . . . . . . . . . . . . . . . . 865.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 875.4 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . 885.4.1 Formulating the Machine Teaching Problem . . . . . 885.4.2 Incorporating Entropy to Optimize Demonstrations . 895.4.3 Application to Task-Specific Success Criteria . . . . . 905.4.4 Visual Guidance Using Augmented Reality (AR) . . . 925.4.5 Task Learning . . . . . . . . . . . . . . . . . . . . . . 925.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 935.5.1 Experimental Task . . . . . . . . . . . . . . . . . . . 935.5.2 Performance Metrics . . . . . . . . . . . . . . . . . . 945.5.3 Preliminary Experiment: Guidance Rule in Simula-tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 955.5.4 User Study Experiment . . . . . . . . . . . . . . . . . 97xTable of Contents5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045.7 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . 1076 Conclusions and Future Work . . . . . . . . . . . . . . . . . . 1086.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1086.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 111Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114AppendicesA Task learning using TP-GMM . . . . . . . . . . . . . . . . . . 133A.1 Task Space Learning . . . . . . . . . . . . . . . . . . . . . . . 133A.2 Joint Space Learning . . . . . . . . . . . . . . . . . . . . . . 134A.2.1 Closed-Loop Inverse Kinematics . . . . . . . . . . . . 135A.2.2 Kinematic Singularities Avoidance . . . . . . . . . . . 135A.2.3 Null Space Optimization . . . . . . . . . . . . . . . . 135A.2.4 Joint-Limit Avoidance . . . . . . . . . . . . . . . . . 136B Legibility Calculation . . . . . . . . . . . . . . . . . . . . . . . 137C Consistency Metrics . . . . . . . . . . . . . . . . . . . . . . . . 138C.1 Summary of Metric Ranges for Consistency Clusters . . . . . 138C.2 Consistency Impact Across Different Learning Methods . . . 138D Questionnaires . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141D.1 Previous Robotics Experience . . . . . . . . . . . . . . . . . 141D.2 Open-ended Questions . . . . . . . . . . . . . . . . . . . . . 145E Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . 149E.1 Regression analysis of task success rate . . . . . . . . . . . . 149E.2 Regression analysis of generalized success rate . . . . . . . . 150xiList of Tables2.1 2x2x2 Mixed Model ANOVA Results of Task PerformanceSuccess Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.2 2x2x2 mixed model ANOVA results of task generalization suc-cess rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.1 Proposed quality metrics. The variables denoted here are: xfor end-effector position, q for joint angle, R for rotation ma-trix, x¨ for end-effector acceleration,...x for end-effector jerk,...q for joint jerk, Ï for torque, J for the Jacobian, nd for thetotal number of degrees of freedom (DOFs), qâd for the lowerlimit of the dth joint, q+d for the upper limit of the dth joint.P (Gi|ξ1:t) is the posterior probability of goal Gi given thetrajectory segment ξ1:t. . . . . . . . . . . . . . . . . . . . . . . 363.2 Results of MANOVA and Univariate Tests for Button-Pressingand Pick-and-Place Experiments . . . . . . . . . . . . . . . . 463.3 Repeated ANOVA Results for Task Performance Success Rates 503.4 Repeated ANOVA Results for Generalized Success Rates . . . 504.1 NASA TLX dimensions [1] for each training group at Baselineand Retention phases (21-point scale). . . . . . . . . . . . . . 75C.1 Average range values for each metric in consistent vs. incon-sistent demonstrations for both experiments. . . . . . . . . . 139xiiList of TablesE.1 Regression Model detailing the relation between the range ofthe proposed metrics and the task success rate in the first ex-periment. x1 represents the range of pathlength in Cartesianspace, x2 represents the range of pathlength in joint space, x3represents the range of path orientation length, x4 representsthe range of jerk in Cartesian space, x5 represents the rangeof jerk in joint space, x6 represents the range of manipula-bility, x7 represents the range of the distance to joint limits,x8 represents the range of the demonstration curvature, x9represents the range of joint effort, x10 represents the rangeof the legibility and x11 represents the consistency. . . . . . . 152E.2 Regression Model detailing the relation between the range ofthe proposed metrics and the task success rate in the sec-ond experiment. x1 represents the range of pathlength inCartesian space, x2 represents the range of pathlength in jointspace, x3 represents the range of path orientation length, x4represents the range of jerk in Cartesian space, x5 representsthe range of jerk in joint space, x6 represents the range of ma-nipulability, x7 represents the range of the distance to jointlimits, x8 represents the range of the demonstration curva-ture, x9 represents the range of joint effort, and x10 representsthe consistency. . . . . . . . . . . . . . . . . . . . . . . . . . . 153E.3 Regression Model detailing the relationship between the rangeof the proposed metrics and the generalized success rate inthe first experiment. x1 represents the range of pathlengthin Cartesian space, x2 represents the range of pathlength injoint space, x3 represents the range of path orientation length,x4 represents the range of jerk in Cartesian space, x5 repre-sents the range of jerk in joint space, x6 represents the rangeof manipulability, x7 represents the range of the distance tojoint limits, x8 represents the range of the demonstration cur-vature, x9 represents the range of joint effort, x10 representsthe range of legibility and x11 represents the consistency. . . . 154xiiiList of TablesE.4 Regression Model detailing the relation between the range ofthe proposed metrics and the generalized success rate in thesecond experiment. x1 represents the range of pathlength inCartesian space, x2 represents the range of pathlength in jointspace, x3 represents the range of path orientation length, x4represents the range of jerk in Cartesian space, x5 representsthe range of jerk in joint space, x6 represents the range of ma-nipulability, x7 represents the range of the distance to jointlimits, x8 represents the range of the demonstration curva-ture, x9 represents the range of joint effort, and x10 representsthe consistency. . . . . . . . . . . . . . . . . . . . . . . . . . . 155xivList of Figures1.1 An illustration of the Learning from Demonstration (LfD)system pipeline. . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Structural overview of the thesis, illustrating the chapter-by-chapter progression toward enabling end users to teach robotsefficiently and effectively. . . . . . . . . . . . . . . . . . . . . . 62.1 The proposed approach for assessing demonstrations qualityusing learned task performance in a task . . . . . . . . . . . . 122.2 Human demonstrators kinesthetically teaching a robot to pressa button on a box: (a) a low-constraint scenario where thebutton is easily reachable by the robot, and (b) a high-constraintscenario requiring maneuvering in a tight space to reach thebutton. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.3 The proposed approach for assessing demonstration qualitywith the button pressing task in two levels of constraints . . . 172.4 Simulation setup used to evaluate the modelâs performanceon (a) the same demonstrated task states and (b) generalizedstates. The buttons are represented as green spheres. Theblue lines represent the trajectories from the initial positionto the button. This figure represents the evaluation of thelearning model as in Section 2.5.1 and Section 3.4.3. . . . . . 192.5 Average success rate distribution of task performance of Session-1 on both task constraints. . . . . . . . . . . . . . . . . . . . 202.6 The average success rate of the learned task for both slow-and fast- adapters in the two sessions for the high and lowconstraint tasks. . . . . . . . . . . . . . . . . . . . . . . . . . 212.7 The success rate of the learned task for both slow- and fast-adapters in all trials. Blue bars represent Session-1 trials andred bars represent Session-2 trials. . . . . . . . . . . . . . . . 23xvList of Figures2.8 The average generalization success rate for both slow- andfast-adapters in the two sessions for the high- and low- con-straint tasks. . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.9 The success rate of task generalization for both slow- andfast-adapters in all trials. Blue bars represent Session-1 trialsand red bars represent Session-2 trials. . . . . . . . . . . . . . 253.1 Overview of the proposed approach. Data was collected fromusers with varying levels of expertise in robotics. The rangeof metrics listed in Table 3.1 was calculated to serve as featureinputs for the clustering algorithm. The data was then clus-tered into two groups: consistent and inconsistent. A learningmodel was subsequently trained using consistent and inconsis-tent data. Finally, performance was evaluated by calculatingthe success rate of the trained models. . . . . . . . . . . . . . 323.2 Button-pressing task demonstration overview from (a) the ini-tial position of the robot, (b) an example of a userâs struggleto get the robot around the box, (c) an example of a bettermanoeuvre of the robot around the box, and (d) the robotpressing the button. . . . . . . . . . . . . . . . . . . . . . . . 373.3 Task demonstration overview: (a) Robotâs initial position,(b) Picking up the bottle, (c) Maneuvering under the obsta-cle, (d) Placing the bottle, (e) Top view of the pickup, ob-stacle, and placement locations, and (f) Experimental bottlewith a spillage line. This figure provides an overview of theexperiment described in Section 3.4.2 and Section 4.4.1. . . . 383.4 The task simulation includes both (a) the same demonstratedstates and (b) the generalized states used to evaluate modelperformance. The blue segment of the trajectory representsthe movement from the initial position to the pickup location,while the green segment represents the movement from thepickup to the place location. . . . . . . . . . . . . . . . . . . . 423.5 The standardized feature values in the resulting two clustersof demonstrations. The first cluster (blue) includes consis-tent demonstrations and the second cluster (orange) includesinconsistent demonstrations. . . . . . . . . . . . . . . . . . . . 473.6 Average task performance success rate across trials/phasesfor both consistent and inconsistent groups. . . . . . . . . . . 483.7 Average generalized performance success rate across trials/phasesfor both consistent and inconsistent groups. . . . . . . . . . . 48xviList of Figures3.8 Heatmap shows the correlation matrix of the task successrate, and the range of the metrics and the consistency. Themetrics are defined in Table 3.1 . . . . . . . . . . . . . . . . . 523.9 Heatmap shows the correlation matrix of the generalized suc-cess rate, and the range of the metrics and the consistency.The metrics are defined in Table 3.1 . . . . . . . . . . . . . . 534.1 Overview of the proposed training methodologies for improv-ing demonstration quality. Two training methodologies lever-age expert data, where users undergo training before practis-ing with the robot. These were compared to discovery train-ing, in which users engage in unguided practice with the robot. 634.2 (a) Top view of the experimental setup for the writing task,and (b) Side view of the experimental setup showing the grip-per holding the marker in a custom compliance holder. . . . . 644.3 Overview of the user study procedure: (1) Baseline: Usersprovided baseline demonstrations from all four pickup loca-tions, (2) Feedback: The robot replayed the demonstrationfrom position 4, (3) Training: Each user was assigned to oneof the three training groups, (4) Retention test: Users pro-vided four demonstrations, replicating the baseline procedure,and (5) Transfer test: Users provided four demonstrationsfor the writing task. . . . . . . . . . . . . . . . . . . . . . . . 654.4 The boundary in the writing task with the 16 generated gen-eralized points at the start and end locations for evaluatingmodel generalization. . . . . . . . . . . . . . . . . . . . . . . . 684.5 Demonstration time across the baseline, retention, and trans-fer phases for the Discovery, Observational, and Kinesthetictraining groups. Error bars represent 95% confidence inter-vals (CI). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714.6 Task performance success rates for the models learned fromparticipant data across the baseline, retention, and trans-fer phases for the Discovery, Observational, and Kinesthetictraining groups. Error bars represent 95% confidence inter-vals (CI). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 724.7 Generalized performance success rates across the baseline, re-tention, and transfer phases for the Discovery, Observational,and Kinesthetic training groups. Error bars represent 95%confidence intervals (CI). . . . . . . . . . . . . . . . . . . . . 73xviiList of Figures4.8 Boxplots of NASA TLX dimensions comparing Baseline andRetention phases across training groups (Discovery, Observa-tional, Kinesthetic). . . . . . . . . . . . . . . . . . . . . . . . 754.9 SUS scores across the three training groups (Discovery, Ob-servational, Kinesthetic). . . . . . . . . . . . . . . . . . . . . . 765.1 System Overview: Using information entropy for guiding usersto the highest uncertainty areas in LfD. . . . . . . . . . . . . 835.2 Overview of the user study procedure. (1) Baseline: Theuser provides as many demonstrations as they think wouldbe sufficient for the robot to learn the task. (2) Training:The user is guided to the uncertain areas for three trials. (3)Retention test: The user is evaluated on the same taskwithout guidance. (4) Transfer test: The user teaches therobot to move from a specific starting point to a wider goalzone without guidance. . . . . . . . . . . . . . . . . . . . . . . 885.3 Experimental setup used in the user study. . . . . . . . . . . 945.4 The average number of demonstrations and robot learningefficiency for both the entropy rule and the heuristic rule withplanner and expert demonstrations. . . . . . . . . . . . . . . . 955.5 Performance metrics for comparing heatmap and single-pointgroups in baseline, retention, and transfer tests. . . . . . . . . 965.6 The number and distribution of the demonstrations in thestarting zone for the baseline, retention, and transfer tests forboth the (a) heatmap group and (b) single-point group. Usersprovided a high number of demonstrations and distributedthem across the starting zone in the baseline. After training,they provided a lower number of demonstrations focused onthe corners. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1015.7 An example of high-, mixed-, and low-quality demonstrations,illustrating their relationship to learning efficacy and theirdistribution across the task space. . . . . . . . . . . . . . . . 1025.8 Participants response to NASA-TLX [1] questions after base-line and retention test. There is no significant difference be-fore and after the training. . . . . . . . . . . . . . . . . . . . . 104C.1 Average task and generalized performance success rates in thepick-and-place task using the diffusion model. . . . . . . . . . 140xviiiAcknowledgementsThe PhD journey has been a long and deeply rewarding one, and it wouldnot have been possible without the support, guidance, and encouragementof many wonderful individuals.First and foremost, I would like to express my deepest gratitude to mysupervisors, Dr. Elizabeth Croft, Dr. Dana Kulic´, and Dr. Mike Van derLoos. Your mentorship has shaped not only the direction of this researchbut also who Iâve become as a researcher and person. I still remembermy very first meeting with Elizabethâher energy, warmth, and passion forresearch left a lasting impression. Thank you, Elizabeth, for your wisdom,care, and steadfast belief in me. It has been a privilege to learn from you.I am also grateful to Dana for her technical insights and for sharing herinfectious enthusiasm for diverse research endeavours. Mike, thank you foryour guidance and out-of-the-box ideas throughout this journey. I wouldalso like to sincerely thank Dr. Dominic Liao-McPherson for stepping inafter Mikeâs retirement and for taking care of all the administrative workwith great support.I also extend my thanks to my committee members, Dr. Michiel van dePanne and Dr. Tim Salcudean, for their valuable time, constructive feed-back, and thoughtful discussions throughout this process. I am especiallygrateful to my examining committee, Drs. Robert Rohling, Chao Chen,Kerstin Dautenhahn, and Wafa Johal, for their thoughtful questions andinvaluable feedback, which helped refine the final version of this work.I am sincerely grateful to my collaborators, labmates, and colleagues atUBC, Monash, and beyond, whose ideas and encouragement helped shapethis work. To the CARIS Lab members at UBC, Wesley Chan, CamiloPerez Quintero, Mahsa Khalili, and Sara Sheikholeslami, thank you for yourcollaboration and inspiration. To the robotics group at Monash, LeiminTian, Tina Wu, Nicole Robinson, Pamela CarrenËo, Rachel Love, and AlexCalvert, it was a privilege to learn and grow alongside you.One of the greatest joys of this journey has been mentoring undergradu-ate students. Jesse Li, Benji Li, Zhikai Zhang, Martin Freeman, and others,thank you for bringing your creativity and energy to our projects. WorkingxixAcknowledgementswith you constantly reminded me why I love research and teaching.I am very grateful for the generous financial support we received for thiswork including, the Alexander Graham Bell Canada Graduate Scholarship,the Michael Smith Foreign Study Supplement, Canada Graduate Scholar-ships, British Columbia Government, and Monash International Postgrad-uate Research, which enabled me to pursue this research and build thefoundation of my academic career.On a personal note, this journey would have meant nothing without thepeople who held me up when I felt like falling. To my mother, Malaka, yourstrength is unmatched. Thank you for your unwavering belief in me, andfor flying across oceans just to be here when I needed you the most. To mylate father, Gamal, and my late brother, Mohamed, your love, wisdom, andpresence are still with me every step of the way. I miss you both more thanwords can say, and I carry your memory into everything I do. To my sister,Fatma, thank you for being there, always. Even from halfway around theworld, your support and prayers never failed to reach me.To my husband, Alaa, my rock, my greatest supporter, and my bestcollaborator. Thank you for your love, patience, and encouragement throughevery late night and tough decision. I couldnât have done this without you.And to my children, Mariam and Yusuf, you are my heart. You filled thisjourney with laughter, joy, and chaos in the best way possible. You remindme every day why this work matters. I hope this thesis teaches you thatwith love, effort, and faith, anything is possible.xxDedicationDedicated to the loving memory of my father, Gamal Sakr, and my brother,Mohamed Sakr. Your love, support, and unwavering encouragement con-tinue to inspire me every day. May your souls rest in peace.xxiChapter 1Introduction11.1 MotivationAccording to the International Federation of Robotics (IFR), the numberof industrial robots operating in factories worldwide reached approximately4.28 million units in 2024, marking a 10% increase from the previous year [2].Meanwhile, service robots are increasingly prevalent, with sales of profes-sional service robots rising by 30% in 2024 [3]. Projections indicate that theadoption of robotic devices will continue expanding across various sectors, in-cluding manufacturing, healthcare, agriculture, and domestic environments.Estimates suggest that the total number of robots in use worldwide couldreach 20 million units by 2030 [4]. This rapid integration highlights the ur-gent need for intuitive and effective interaction methods for everyday users,most of whom lack expertise in robotics or programming.This thesis aims to empower non-expert users to program robots intu-itively and efficiently, enabling them to teach robots new skills and behaviorswithout requiring technical knowledge. The overarching goal is to bridge thegap between human expertise and the powerful capabilities of robotic sys-tems, making robotics more accessible across diverse applications.Consider a scenario in which a household robot is tasked with makingand serving coffee. While the robot may be pre-programmed to brew andpour coffee at the press of a button, user preferences vary, some may storesugar in a specific drawer, while others keep mugs in a designated cupboard.A one-size-fits-all approach would be inadequate in such a setting. To servecoffee as desired, the robot must learn and adapt to individual preferencesand the unique layout of each home. Furthermore, this adaptation shouldoccur naturally and intuitively, without requiring expert intervention. Thisexample underscores the necessity of seamless, intuitive communication be-tween users and robots.Robot Learning from Demonstration (LfD) enables robots to acquire1A version of this chapter was presented inMaram Sakr.âTowards an Efficient Teach-ing by Demonstration Framework for Robot Learningâ, Companion of the Robotics: Sci-ence and Systems (RSS), RSS Pioneers workshop, July 2023. (Acceptance rate: â¼ 22%)11.1. Motivationnew skills and behaviors by observing human demonstrations, minimizingthe need for explicit programming [5â7]. This approach not only reduces thecomplexity of the search space for learning but also enables users to conveytask information through intuitive and familiar actions, for example, phys-ically guiding a robot arm, gesturing toward objects, or teleoperating therobot, rather than writing code. Such interactions are termed ânaturalâ herenot to imply a single âtypicalâ user, but rather to indicate that LfD can beadapted to the modalities most accessible to each individual. This flexibilitymakes LfD particularly advantageous for novice users with diverse abilitiesand preferences, as the teaching interface can be designed to accommodatedifferent heights, motor abilities, sensory impairments, or interaction styles,lowering the barrier to participation compared to programming-based ap-proaches [8, 9].While LfD holds significant promise, many existing learning algorithmsrely on standard machine learning techniques that assume optimal data [10,11]. However, human-provided demonstrations often introduce challengessuch as limited patience, suboptimal demonstrations, and inefficient datacollection [12â14]. Factors like fatigue, lack of experience, and environmen-tal constraints can further degrade demonstration quality, creating a gap be-tween algorithmic assumptions and real-world user behavior [15â17]. Overthe past three decades, research in LfD has advanced task representations,learning algorithms, and interaction interfaces [5, 10, 18, 19]. However, mak-ing LfD intuitive and effective for non-expert users remains a challenge, asmany users lack an understanding of how robots learn or what constitutes aneffective demonstration for generalization. Studies indicate that the qualityof input data impacts generalization more significantly than modificationsto the learning algorithm itself [20]. Consequently, this thesis focuses onimproving demonstration quality to enhance LfD effectiveness, particularlywhen demonstrations are provided by novice users.A key challenge in LfD is to obtain high-quality, informative demonstra-tions efficiently while minimizing user burden. This thesis addresses thischallenge by investigating the factors that contribute to effective learningand generalization in LfD. Additionally, it proposes training and guidancestrategies to help non-expert users improve their teaching skills, ensuringthat the demonstrations they provide are more effective and informative.21.2. BackgroundFigure 1.1: An illustration of the Learning from Demonstration (LfD) sys-tem pipeline.1.2 Background1.2.1 Robot Learning from DemonstrationsRobot Learning from Demonstration (LfD) explores techniques for learninga task policy from examples provided by a human teacher [5, 9, 10, 19, 21â23]. LfD is particularly advantageous when ideal behavior cannot be easilyscripted (as in traditional robot programming) or formulated as an opti-mization problem but can instead be demonstrated. This approach is alsoreferred to in the literature as Programming by Demonstration (PbD) [24],learning by showing [25], imitation learning [19], and apprenticeship learn-ing [22].Typically, LfD involves a human demonstrator teaching a task to a robotusing a specific teaching interface. Demonstrations generally consist of state-action pairs, from which a policy is learned. This policy maps perceivedstates to appropriate actions, enabling the robot to reproduce the demon-strated behavior and generalize it to different scenarios, as illustrated inFigure 1.1.Enhancing the effectiveness of LfD requires improvements across the en-tire pipeline, including learning algorithms, teaching interfaces, and demon-stration quality. While much of the existing literature has focused on ad-vancing learning algorithms [10, 18] and developing intuitive teaching in-terfaces [9, 26, 27], less attention has been given to the role of the humanteacher [11, 13, 16]. The choice of the teaching interface influences not onlythe type and modality of demonstration data but also the user experience,including intuitiveness, cognitive load, and physical effort [28, 29]. Variousteaching interfaces have been proposed, each designed for specific tasks, usergroups, and robotic platforms [5, 23, 30, 31].Despite these advancements, most studies rely on researchers or experi-enced individuals to provide demonstrations, ensuring high-quality train-31.2. Backgrounding data. Some studies attempt to mitigate the impact of suboptimaldemonstrations by artificially injecting noise to simulate real-world imperfec-tions [32], while others treat unsuccessful demonstrations as negative exam-ples for learning [33]. Additionally, some approaches assume access to expertdemonstrations for comparison, discarding low-quality demonstrations [34]or refining them through further interactions with the demonstrator [35].However, collecting demonstrations is time-consuming and costly, making itmore practical to maximize the usefulness of all available data and supportusers in providing high-quality demonstrations from the outset.This thesis shifts the focus from post hoc correction of demonstrationquality to its proactive improvement at the source. It introduces methods forevaluating demonstrations without requiring expert comparisons and pro-poses strategies to guide non-expert users in providing high-quality demon-strations from the outset, thereby reducing reliance on costly corrective mea-sures.1.2.2 Teaching InterfacesHumans can transfer their skills to robots using various teaching interfaces,each with its own advantages and challenges. Ravichandar et al. [10] cate-gorize demonstration interfaces into three types: passive observation, kines-thetic teaching, and teleoperation.Passive observation involves users performing the task using their ownbody, sometimes with additional sensors to facilitate tracking [36, 37]. Therobot does not participate in the task execution but instead passively ob-serves the demonstrator. While this method requires minimal training forthe user, it presents challenges such as occlusions, rapid movement, and sen-sor noise in capturing human actions. Additionally, passive observation oftennecessitates learning a mapping from human actions to robot-executable ac-tions due to perceptual and physical mismatches, a challenge known as thecorrespondence problem [38, 39].Kinesthetic teaching addresses this issue by allowing the human teacherto physically guide the robot through the task [17, 40]. The robotâs sensorsdirectly record the motion data, eliminating the need to map human kine-matics to robot kinematics. This method is widely used for human-scalemanipulators, including lightweight industrial robots, due to its intuitiveapproach and minimal user training requirements. Moreover, kinestheticteaching only requires the robotâs onboard sensors and does not rely on ad-ditional tracking systems. However, it may not be suitable for large robotsdue to concerns related to inertia and safety [41].41.2. BackgroundTeleoperation interfaces provide another alternative by enabling theoperator to control the robot remotely, reducing the risk of harm [42, 43].As with kinesthetic teaching, the motion data is recorded directly from therobotâs sensors, avoiding the correspondence problem. Teleoperation typi-cally involves external input devices such as joysticks or graphical user inter-faces. However, it presents challenges such as limited situational awarenessof the robotâs task space, additional effort required to develop the interface,and the need for user training, especially for robots with many degrees offreedom [44].This thesis focuses on kinesthetic teaching and teleoperation to avoid thecorrespondence problem while enabling effective user-driven robot learning.1.2.3 Human Teachers in LfDWhile much of the LfD literature focuses on improving learning algorithmsand optimizing robot task execution, significantly less attention has beengiven to the role of the human teacher. LfD depends on human demon-strations as the primary source of training data, making the quality, con-sistency, and informativeness of these demonstrations crucial for successfulrobot learning. However, human teachers vary significantly in their teach-ing styles, experience levels, and ability to adapt over time [45â47]. Thesefactors influence the effectiveness of demonstrations and, consequently, thequality of learned policies.A robotâs performance is highly dependent on the quality of the demon-strations it receives [5, 48]. Demonstration quality is often assessed basedon optimality and sufficiency. A demonstration is considered optimal if itfollows the most efficient policy, and a set of demonstrations is deemed suffi-cient if it allows the model to generalize across the entire task space [49, 50].However, novice users frequently provide suboptimal and insufficient demon-strations, limiting the robotâs ability to learn effectively.Although much of the LfD literature assumes that demonstrations areprovided by experts, real-world scenarios often involve non-expert users whointroduce ambiguity or inefficiencies into their demonstrations [51, 52]. Sub-optimal demonstrations can result from several factors, including task com-plexity [53], lack of domain expertise [54], limited experience in robotics,and challenges posed by the correspondence problem [9].Studies have shown that non-expert teaching styles vary significantly andevolve over multiple teaching sessions [14, 55]. Aliasghari et al. [56] foundthat some non-experts adopt structured and repetitive teaching strategies,while others favour exploratory or inconsistent approaches. While certain51.2. BackgroundFigure 1.2: Structural overview of the thesis, illustrating the chapter-by-chapter progression toward enabling end users to teach robots efficientlyand effectively.individuals improve their teaching effectiveness over time, others struggle toadapt, leading to inconsistent or incomplete demonstrations. This variabilityhighlights the need for adaptive teaching interfaces and training methods tosupport non-experts in providing high-quality demonstrations.The impact of suboptimal demonstrations on robot learning is signifi-cant. Poor-quality data can cause the robot to learn incorrect or inefficientpolicies, resulting in suboptimal task execution or, in some cases, failureto learn the task at all [5, 52]. Additionally, suboptimal demonstrationsincrease learning time and data requirements, as robots often require addi-tional demonstrations or corrective feedback to compensate for poor trainingdata [56]. They can also introduce bias in learned behaviors, as inconsis-tent demonstrations may cause the model to overfit to specific conditions,reducing its ability to generalize across different scenarios [57].This thesis focuses on addressing these challenges by improving the abil-ity of human teachers to provide high-quality demonstrations while con-trolling for factors such as the correspondence problem and domain-specificexpertise. By evaluating and enhancing human teaching skills through struc-tured feedback, improved interfaces, and interactive teaching methodologies,we aim to bridge the gap between human demonstration quality and opti-mal robot learning outcomes. Understanding the role of human variabilityin LfD and implementing targeted strategies for improving teaching effec-tiveness can lead to more robust and adaptable robotic learning systems.61.3. Approach1.3 ApproachThis thesis focuses on improving the quality and distribution of demonstra-tions to enhance task learning and generalization in robot learning fromdemonstration (LfD). The primary goal is to improve demonstration qualityat its source (i.e., the human teacher) by enhancing their teaching skills andhelping them better understand the robotâs learning needs. This improve-ment ensures that robots receive high-quality demonstrations that facilitateefficient learning and generalization while minimizing unnecessary user ef-fort.To achieve this, the thesis first establishes clear quantitative metrics toevaluate demonstration quality. These metrics allow us to assess whether ademonstration is informative, consistent, and beneficial for learning. Addi-tionally, user adaptation levels are considered, as variations in user expertiseand teaching strategies affect demonstration quality. Several factors influ-ence adaptation, including prior experience, task complexity, and the robotâsdegrees of freedom. By measuring adaptation levels, training can be tailoredto each userâs needs, ensuring they provide optimal demonstrations.Another key focus of this thesis is enhancing user training methodolo-gies. Existing teaching strategies are often designed for human learnersrather than machine learners [58]. However, human teachers can adapt theirteaching styles to different learners, including robots [59]. To facilitate thisadaptation, training methodologies are introduced to transfer expert roboti-cistsâ skills to everyday users without requiring direct expert involvement.Expert demonstration data is recorded and leveraged for user training, en-abling users to improve their demonstration quality more efficiently thantraditional self-discovery training, where users practice independently.Additionally, demonstration efficiency is a critical aspect of this thesis.Efficiency is defined as achieving high task learning and generalization per-formance with the fewest possible demonstrations. To prevent data sparsityand unbalanced demonstration distribution, a guidance system is proposedto help users select training points that maximize learning performance.This is achieved through task-related information entropy, which quantifiesuncertainty in the robotâs task execution across different workspace regions.Users are guided toward uncertain areas during initial training trials, al-lowing them to develop an intuitive sense of demonstration distribution.Afterward, they are expected to teach robots without guidance, ensuringlong-term effectiveness.71.4. Thesis Objectives1.4 Thesis ObjectivesThis thesis aims to enable lay users to effectively teach robots throughdemonstrations while ensuring that robots receive sufficient and informa-tive demonstrations to optimize learning and generalization. The researchobjectives, illustrated in Figure 1.2, are structured as follows:1. Develop metrics to quantify demonstration quality and as-sess their impact on the robotâs learning and generalizationperformance.Two complementary approaches are proposed for evaluating demon-stration quality. First, quality is assessed based on how well the learn-ing algorithm reproduces the demonstrated task, providing a directmeasure of its contribution to learning and generalization. This eval-uation also serves to infer user adaptation levels, offering insights intohow different users progress over time. Second, demonstration qualityis analyzed using motion characteristics that reflect consistency, allow-ing for an independent assessment that does not require training thelearning algorithm or relying on expert reference data. By establishingreliable quality metrics, the aim is to ensure that only high-quality datais used in learning, thereby improving overall system performance.2. Design and evaluate training approaches to help novice usersimprove their teaching skills, thereby enhancing the qualityof their demonstrations.Building on insights from the first objective, various training method-ologies are designed and evaluated to assist users in providing high-quality demonstrations. Lay users often struggle with kinestheticteaching due to unfamiliarity with robot kinematics and constraints,leading to suboptimal demonstrations. To address this, observationaltraining and kinesthetic training are introduced as alternative methodsto transfer expert skills without direct expert supervision. Observa-tional training exposes users to expert demonstration videos, whilekinesthetic training allows users to physically experience expert move-ments by passively holding the robot as it replays an expert demon-stration. These training methods are compared against self-discoverylearning, where users practice independently, to assess their effective-ness in improving teaching quality and efficiency.3. Establish criteria for identifying the most informative demon-strations within the task space and provide guidance to users81.5. Thesis Organizationon optimizing demonstration placement.To optimize demonstration distribution, task-related information en-tropy is used to identify high-uncertainty regions where additionaldemonstrations would be most beneficial. An augmented reality (AR)-based guidance system is developed to visually direct users to criticalareas, serving as an initial training tool to help users learn how to ef-ficiently distribute demonstrations. Their ability to generalize demon-stration placement is then evaluated on both the same task (retentiontest) and a new task (transfer test) to assess long-term teaching effec-tiveness.1.5 Thesis OrganizationThe remainder of this thesis is organized as illustrated in Figure 1.2 andstructured as follows:Chapter 2 presents the first step in quantifying demonstration quality byassociating it with learning algorithm performance, addressing the first ob-jective. A user study (N = 27) was conducted in which participants taughta PR2 robot a button-pressing task under different constraints. Teachingbehavior was captured across two sessions to analyze variations over time.Results revealed a strong correlation between task performance and gener-alization ability, as well as two distinct user clusters: fast adapters, whoprovided high-quality demonstrations immediately, and slow adapters, whoimproved with practice. This study highlights the need for targeted trainingto support slower-adapting users. This work was published in [60].Chapter 3 extends the first objective by introducing consistency met-rics to predict learning and generalization performance. Unlike Chapter 2,which focuses on learning outcomes, this chapter evaluates motion charac-teristics as quality indicators to assess demonstrations before training. Theproposed approach is validated using data from two user studies: the firststudy reuses the dataset from Chapter 2, where participants taught a PR2robot a button-pressing task, while the second study (N = 30) involves par-ticipants training a UR5 robot on a pick-and-place task. Results show thatdemonstration consistency significantly impacts learning success, with ourmetrics predicting 70% and 89% of task success rates in the two studies andestimating generalization performance with 76% and 91% accuracy. Thesefindings demonstrate that consistency-based assessment provides a reliableway to evaluate demonstration quality before training, reducing the need forexpert data and algorithm-specific modifications. This work was published91.5. Thesis Organizationin [61, 62]Chapter 4 addresses the second objective by proposing two expert-driventraining methodologiesâobservational and kinesthetic training. A user study(N = 30) compared these methods against discovery learning, where userspracticed independently. Results showed performance improvements in theobservational and kinesthetic groups, with success rates increasing by 16%and 20%, respectively. Additionally, training time significantly decreased,demonstrating that expert exposure enhances training efficiency and effec-tiveness. This work is published in [63, 64]Chapter 5 addresses the third objective by proposing a task-related en-tropy method to guide users in demonstration placement. A user study (N= 24) used an AR-based system to direct users toward high-entropy regions.After initial training, users were tested on both retention and transfer taskswithout guidance. The results showed up to 198% improvement in tasksuccess and a 210% improvement over a state-of-the-art heuristic method,confirming that entropy-based guidance significantly enhances learning effi-ciency. This work is published in [65].The thesis concludes with Chapter 6, which summarizes the key findingsand contributions of this work and outlines potential directions for futureresearch. Additional proofs, implementation details, questionnaires, andexperimental results are presented in the Appendix.10Chapter 2Quantifying DemonstrationQuality for Robot Learningand Generalization22.1 IntroductionThis chapter addresses the first objective by proposing a simple yet effectivemethodology for quantifying demonstration quality based on the robotâsperformance in reproducing the task. Additionally, we use this evaluationto assess how different users adapt to the task and the robot over time.As noted in [5], a robotâs performance depends heavily on the quality ofthe demonstrations it receives. Therefore, it is crucial to define and mea-sure demonstration quality when teaching a robot. Various factors influencedemonstration quality, including the experience and adaptation level of thehuman teacher [45, 46]. A userâs adaptation to the task and the robot mayvary based on their prior expertise. Kobak and Mehring [66] highlightedthat individuals tend to learn new tasks of the same type more efficientlyafter practicing motor tasks with structural similarities. However, statedrobotics expertise alone may not be a reliable indicator of a userâs teach-ing proficiency [67]. This necessitates a quantitative approach to assessingdemonstration quality, rather than relying solely on self-reported expertise.The term âpoor-quality demonstrationsâ has been broadly used in theliterature to describe various issues in the collected data. These issues in-clude undesired motions [18], failed demonstrations [33], and ambiguousdemonstrations [13]. However, defining and measuring demonstration qual-ity explicitly remains an open research question [18].While successful demonstrations that achieve the task goal without vio-lating constraints are easy to verify when task constraints are well-defined,2This chapter is published inMaram Sakr, Zexi Jesse Li, H.F. Machiel Van der Loos,Dana Kulic´, and Elizabeth Croft. âQuantifying Demonstration Quality for Robot Learningand Generalizationâ, IEEE Robotics and Automation Letters, 7(4), pp. 9659-9666, 2022.112.2. Related WorkFigure 2.1: The proposed approach for assessing demonstrations qualityusing learned task performance in a taskfailed demonstrations are more ambiguous. It is not always clear whether afailed demonstration is entirely incorrect or if it was mostly correct but faileddue to a specific error. This ambiguity makes unsuccessful demonstrationsdifficult to exploit in robot learning [48, 68].In this chapter, we define demonstration quality based on how well thelearning algorithm reproduces the task, rather than relying on the demon-stratorâs stated expertise level. This is achieved by evaluating the learningalgorithm on both the training task set and a generalized version of the task.Furthermore, by analyzing the quality of demonstrations across repeated tri-als, we assess usersâ adaptation levels to the task and their improvement overtime.2.2 Related WorkSeveral studies have attempted to assess the quality of human-provideddemonstrations. Ureche and Billard [69] proposed three metrics for assess-ing demonstrations in bi-manual tasks: (i) the ability to maneuver the tool,(ii) consistency in teaching, and (iii) the degree of coordination between thetwo arms. However, these metrics focus specifically on bi-manual tasks andmay not be applicable to other task domains.Kaiser et al. [48] identified different sources of sub-optimality in humandemonstrations, including (i) unnecessary actions that do not contribute toachieving the goal, (ii) incorrect actions that negatively impact learning, and(iii) unmotivated actions that are measured by sensors unavailable to the122.3. Proposed Approachrobot. To mitigate unmotivated actions, demonstrations can be recordeddirectly from the robotâs sensors [23]. To filter unnecessary actions, a met-ric was introduced to detect such actions, allowing experimenters to decidewhether to include or discard them. However, distinguishing incorrect ac-tions from correct ones remains a challenging problem [48].Fischer et al. [70] analyzed different teaching interfaces in LfD and iden-tified three common errors made by users: (i) excessive pressure applied tothe gripper, (ii) movement into singular configurations, and (iii) movementinto self-collision. However, they did not investigate how these errors impactrobot learning and generalization.Sena and Howard [13] categorized poor-quality demonstrations into threemain issues: (i) undemonstrated states, where the robot lacks sufficientdemonstrations to perform the task, (ii) ambiguous demonstrations thatprovide little new information to the learning model, and (iii) unsuccess-ful demonstrations that fail to achieve the task goal [33]. To address theseissues, they proposed two metrics: (i) teaching efficacy, which measureshow well the robot generalizes over the task space, and (ii) teaching effi-ciency, which normalizes teaching efficacy by the number of demonstrationsprovided. These metrics help mitigate data sparsity in human demonstra-tions [5], but they do not explicitly assess the quality of individual demon-strations in terms of their contribution to robot learning and generaliza-tion [71].In this chapter, we propose a method that links the quality of demon-strations to their effectiveness in task learning and generalization. Learningis defined with respect to the task space covered by the demonstrations,while generalization is evaluated based on the extent to which the demon-strations enable the robot to perform well in new but similar scenarios.We hypothesize that if demonstrations lead to high task performance intraining conditions, they will also lead to high performance in similar con-ditions. This assumption holds when the learning algorithm is well-suitedto the given data and when the test conditions are drawn from a similardistribution as the training demonstrations [72].2.3 Proposed ApproachSince task performance and generalization are critical objectives in any LfDsystem [18], we propose using these metrics to assess the quality of inputdemonstrations. As illustrated in Figure 2.1, the provided demonstrationsare used to train a learning model, which is then evaluated on the same task132.4. Experimental Designconditions covered by the demonstrations. If the model successfully per-forms the task with a success rate exceeding a predefined threshold (δ), thedemonstrations are classified as high-quality. Otherwise, they are consid-ered low-quality. The success criteria are task-specific and must be definedbeforehand.After classifying the demonstrations, the model is tested on a new taskset to evaluate its generalization performance. This step validates our hy-pothesis that demonstrations leading to high performance on the trainingtask set will also result in strong generalization performance, whereas lower-quality demonstrations will lead to weaker generalization.2.4 Experimental DesignTo evaluate the proposed approach, we designed an experimental setup fora user study to record demonstrations across multiple trials and sessions.The study included tasks with varying levels of constraints to assess userperformance in both simple and complex scenarios.2.4.1 Robot PlatformThe robot platform used in this study is the PR2 (Willow Garage, PersonalRobot 2), a mobile manipulator with two 7-degree-of-freedom (DoF) arms.The passive spring counterbalance system in PR2âs arms provides gravitycompensation, allowing users to kinesthetically move the robotâs arms withintheir kinematic range. Each arm has a 1-DoF underactuated gripper. Forthis experiment, only the right arm was used in gravity compensation modewith the gripper closed.2.4.2 Task DefinitionThe chosen exemplar task is pressing a button, a common action applica-ble to various real-world scenarios, such as pressing a doorbell, an elevatorcall button, or a pedestrian crossing button. This task was selected for itsgeneralizability, ensuring that it does not require domain expertise but stillnecessitates practice with the robot to produce high-quality demonstrations.The task consists of both a constrained reaching motion and fine control forpressing the button.Furthermore, the task was tested under two different levels of configura-tion and task-space constraints: low-constraint and high-constraint condi-tions. This investigation is motivated by Fittsâ law [73], which models task142.4. Experimental Design(a) Low-constraint task (b) High-constraint taskFigure 2.2: Human demonstrators kinesthetically teaching a robot to pressa button on a box: (a) a low-constraint scenario where the button is easilyreachable by the robot, and (b) a high-constraint scenario requiring maneu-vering in a tight space to reach the button.difficulty based on the logarithm of the ratio between the target distanceand the target width. In this study, we use target distance and constraintlevel as key difficulty factors.Figure 2.2 shows the experimental setup used for data collection. Acardboard box was fixed on one of its vertices so that all buttons werewithin reach of the robotâs gripper. Two faces of the box were used fordata collection. On each face, buttons were positioned at the center (largegreen button), at the corners (purple foam markers), and at intermediatelocations between the corners and the center (green foam markers). A totalof nine goal positions were used per face. Face-1 represents a low-constrainttask, where the robot can easily reach all target points, as illustrated inFigure 2.2-a. Face-2 represents a high-constraint task, where participantsmust maneuver the robot arm around the box in a constrained space toreach the goal positions while avoiding self-collisions and contact with thebox, as illustrated in Figure 2.2-b.2.4.3 User StudyWe recruited participants for our user study through advertisements on theuniversity campus and social media. A total of 27 participants (21 male, 6female) with a mean age of 22 years (µage = 22) and varying levels of roboticsexpertise, ranging from no experience to six or more years of experience,were recruited. Prior to the study, informed consent was obtained from152.4. Experimental Designeach participant.The experiment was conducted over two sessions on separate days. Thistwo-session format was chosen based on evidence that practice is essential formotor skill learning [74]. We aimed to investigate which aspects of perfor-mance improved from the first to the second session and how these improve-ments would be reflected in robot learning and generalization. Participantswere scheduled in advance for one-hour time slots. Upon arrival, they weregiven an informed consent form to sign. The experimenter briefly explainedthe long-term goal of the research and informed participants that their taskwas to program new skills into the PR2 robot. Specifically, participantswere told that the PR2 would imitate and learn from their demonstrations,enabling it to press a button located anywhere on the boxâs face after train-ing.The experimenter provided no instructions on how to program the PR2;instead, participants were encouraged to learn through exploration, allow-ing us to capture their first-time (novice) interaction with the robot. Therobot was set in gravity compensation mode, and participants were askedto kinesthetically guide its right arm to press target buttons. The rightarm always started from the same position, with the elbow at 90° and thegripper pointing upward (untucked position). After each demonstration, theexperimenter used a joystick to teleoperate the right arm back to its initialposition.Participants first guided the robot to press the nine buttons on Face-1 (low-constraint task) before progressing to the nine buttons on Face-2(high-constraint task), completing three trials per face. The robotâs jointangles were recorded during each demonstration and saved as ROSbag filesfor offline analysis. Each participant provided a total of 54 demonstrationsper session.At the end of the first session, the experimenter asked participants toreflect on what they had learned about teaching the robot and scheduledthe second session on a different day. This procedure was motivated bythe findings of Walker et al. [75], which suggest that a night of sleep aftermotor skill training significantly enhances skill retention and performancein subsequent sessions.In the second session, each participant repeated the same procedure asin the first session. After completing the second session, participants filledout a questionnaire regarding their previous experience in robotics, if any(as shown in the Appendix D.1). They were explicitly instructed not toinclude the experience gained during the experiment as part of their priorexperience. Additionally, they were asked to provide details about their162.5. AnalysisFigure 2.3: The proposed approach for assessing demonstration quality withthe button pressing task in two levels of constraintsprevious experience to ensure that they did not account for the experiment intheir responses. Participants also answered open-ended questions regardingthe aspects of their performance that they felt had improved between thetwo sessions.2.5 AnalysisDemonstration data from all participants was collected and saved for offlineanalysis. The application of the proposed approach to the experimentaldata is detailed in Figure 2.3. To evaluate the learning modelâs performanceon the same demonstrated states, we used the original button locations, asshown in Figure 2.4a. To assess generalization, we discretized the space intoa 7Ã7 grid, resulting in 49 new target locations, as illustrated in Figure 2.4b.The grid size was chosen based on the dimensions of the PR2 armâs tip andthe box to avoid overlap between targets. The arm tip dimensions are (W= 2.1 cm, L = 2.2 cm, H = 3.5 cm), and the box is a cube with edges of26 cm. The target point was defined as a sphere with a diameter of 3 cm,centred on the button.To define the threshold δ in the proposed approach, we used a data-driven approach using the task performance success rate on the first sessionin the two constraints levels. We used the first session as it reflects theusers first interactions with the robots and will show the variations in usersperformance. We plot the task success rate of the first session of all partic-ipants as a violin chart to examine the distribution, as shown in Figure 2.5.172.5. AnalysisThe violin figure shows a clear bi-modal distribution, especially with high-constraint tasks. This shows that the data is clusterable into two clusters.Since the mean and median of both task constraints are different, the thresh-old is defined as a value between them. A threshold of 80% was chosen tocategorize the data into: high-quality and low-quality.2.5.1 Learning and Generalization EvaluationFor task learning, we employed the Task-Parameterized Gaussian MixtureModel (TP-GMM) [76]. TP-GMMs have been widely used in LfD [18, 77]due to their strong generalization capabilities with a limited number ofdemonstrations. Implementation details for learning the task in both Carte-sian and joint space are provided in Appendix A.A learned trajectory was considered successful if it reached the goalposition (within the goal sphere) while avoiding self-collisions and collisionswith the box. Since the robotâs end-effector has a nonzero size, the goalwas considered reached if any point on the tip touched the goal sphere.The evaluation was performed in RViz and Gazebo, where we simulated theexperimental setup and used the joint-space trajectory to verify whether thelearned trajectory was feasible and to compute the robotâs collision rate.To calculate the success rate for each trial, we divided the number ofsuccessful trajectories by the total number of tested points: nine in the taskperformance evaluation and 49 in the generalization evaluation.2.5.2 User AdaptationThe proposed approach, as shown in Figure 2.3, was used to classify the pro-vided demonstrations from both sessions into low-quality and high-qualitycategories. We identified two distinct groups of participants:â¢ Fast-adapters: Users who consistently provided high-quality demon-strations in both sessions, indicating a quick adaptation to the taskand the robot.â¢ Slow-adapters: Users who initially provided low-quality demonstra-tions in the first session but improved significantly after practice inthe second session.These findings highlight individual differences in learning and adapta-tion, emphasizing the need for training methodologies that accommodatedifferent adaptation speeds.182.6. Hypotheses(a) Same demonstrated states (b) Generalized statesFigure 2.4: Simulation setup used to evaluate the modelâs performance on (a)the same demonstrated task states and (b) generalized states. The buttonsare represented as green spheres. The blue lines represent the trajectoriesfrom the initial position to the button. This figure represents the evaluationof the learning model as in Section 2.5.1 and Section 3.4.3.Interestingly, we examined participantsâ self-reported experience in roboticswithin both groups. We found that 50% of the users in the fast-adaptersgroup claimed prior experience, whereas only 25% of the users in the slow-adapters group reported previous experience. This underscores the impor-tance of assessing demonstration quality before learning, regardless of usersâstated experience, as prior experience in other tasks or domains can betransferable and may contribute to their performance in robot teaching [67].2.6 HypothesesWe expect a strong correlation between task performance and generalizationperformance, as discussed in Section 2.2. Furthermore, we anticipate thatthe robotâs performance in both task execution and generalization will sig-nificantly improve in the second session compared to the first, as users willhave had the opportunity to practice the task through multiple trials. Ad-ditionally, we expect that the fast-adapters will achieve significantly higherperformance than the slow-adapters, as they are able to provide high-qualitydemonstrations more quickly.Based on these expectations, we formulate the following hypotheses:â¢ H1: There is a significant correlation between task performance andgeneralization performance.192.7. ResultsFigure 2.5: Average success rate distribution of task performance of Session-1 on both task constraints.â¢ H2: The task performance of fast-adaptersâ demonstrations is signifi-cantly higher than that of slow-adaptersâ demonstrations.â¢ H3: The task performance using second-session demonstrations issignificantly higher than using first-session demonstrations for slow-adapters.â¢ H4: The generalization performance of fast-adaptersâ demonstrationsis significantly higher than that of slow-adaptersâ demonstrations.â¢ H5: The generalization performance using second-session demonstra-tions is significantly higher than using first-session demonstrations forslow-adapters.2.7 ResultsTo analyze the correlation between task performance and generalization, wefirst assessed the normality of the data before computing the correlationcoefficient. Since the data did not follow a normal distribution, we usedthe Spearman correlation coefficient [78]. The results indicate a statisticallysignificant positive correlation between task performance and generalization(Ï = 0.76, p < 0.0001), supporting H1.202.7. ResultsSlow-adapters Fast-adapters Slow-adapters Fast-adapters00.20.40.60.81Success Rate*******Session 1 Session 2Low-constraint Task High-constraint TaskFigure 2.6: The average success rate of the learned task for both slow- andfast- adapters in the two sessions for the high and low constraint tasks.2.7.1 Task Performance AnalysisFigure 2.6 presents the mean success rate and standard deviation for taskperformance across fast-adapters and slow-adapters, over two sessions andtwo task constraints. Overall, performance in Session-2 was higher withless variance compared to Session-1, across both adaptation groups and taskconstraints. Furthermore, the improvement between sessions was greater forhigh-constraint tasks than for low-constraint tasks. Additionally, while therewas a large performance gap between fast- and slow-adapters in Session-1,this difference decreased in Session-2, indicating an improvement in slow-adapters.To analyze the impact of adaptation level, session progression, and taskconstraints on task performance, we conducted a 2x2x2 mixed model ANOVA.Prior to analysis, the data were assessed for compliance with statistical as-sumptions. Six out of the eight data sets did not follow a normal distribution,but ANOVA tests are known to be robust to violations of normality [79].The excess kurtosis values for these six groups were -0.525, 0.854, 7.561,0.897, 0.538, and 0.522 (where a normal distribution has an excess kurtosisof zero). Apart from one case, the violations were considered minor, allowingus to proceed under the assumption of normality. Leveneâs test confirmedthat all data sets satisfied the homogeneity of variances assumption.Table 2.1 presents the ANOVA results, showing the significance levelsof all factors and their interactions in task performance. The analysis re-vealed that task performance significantly improved in Session-2 compared212.7. ResultsTable 2.1: 2x2x2 Mixed Model ANOVA Results of Task Performance SuccessRateFactor(s) F p-value Levels Marginal MeansSessions 32.94 p < 0.001Session-1 0.70± 0.027Session-2 0.88± 0.018AdaptationLevel2170.77 p < 0.001Slow 0.73± 0.023Fast 0.85± 0.025TaskConstraints15.26 p < 0.001Low 0.86± 0.019High 0.73± 0.028AdaptationLevel XSessions7.36 p = 0.012Slow in Session-1 0.60± 0.037Slow in Session-2 0.86± 0.023Fast in Session-1 0.81± 0.041Fast in Session-2 0.90± 0.026to Session-1, supporting H3. Furthermore, fast-adapters achieved signifi-cantly higher performance than slow-adapters, supporting H2.Pairwise comparisons with Bonferroni correction indicated that slow-adapters exhibited a significant improvement in success rate from Session-1 to Session-2 (p < 0.001). Meanwhile, fast-adapters showed a marginalimprovement (p = 0.053) across sessions. Another key finding is that therewas a significant difference between fast- and slow-adapters in Session-1(p < 0.001), but this difference was not statistically significant in Session-2,suggesting that slow-adapters improved enough to close the initial gap.To further examine user adaptation over time, we analyzed the suc-cess rate per individual trial across both sessions. Figure 2.7 illustrates theprogress curve for both groups. Slow-adapters took longer to achieve highperformance, particularly in high-constraint tasks. Their initial performancein Session-1 trials was low (0.49±0.34) but gradually improved in Session-2,reaching (0.87± 0.22) by the end. Fast-adapters showed a steady improve-ment in Session-1, reaching a high-performance plateau by the sessionâs end(0.90± 0.16). In low-constraint tasks, slow-adapters converged to high per-formance by the end of Session-1 (0.87 ± 0.15). In contrast, fast-adaptersreached a higher performance (0.99±0.032) by the second trial of Session-1.222.7. ResultsSlow-adapters Fast-adapters Slow-adapters Fast-adapters00.20.40.60.81Success RateLow-constraint Task High-constraint TaskFigure 2.7: The success rate of the learned task for both slow- and fast-adapters in all trials. Blue bars represent Session-1 trials and red barsrepresent Session-2 trials.2.7.2 Task Generalization Performance AnalysisFigure 2.8 presents the mean and standard deviation of the success ratein task generalization for both fast- and slow-adapters across two sessionsand two task constraints. The results follow a similar trend to the taskperformance analysis, with Session-2 exhibiting a higher success rate andreduced variance compared to Session-1 for both adaptation groups andtask constraints. Additionally, a notable gap exists between slow- and fast-adapters in Session-1, which narrows in Session-2. Furthermore, while thelow-constraint tasks maintain a consistent success rate across task learningand generalization, the high-constraint tasks experience a degradation insuccess rate during generalization compared to task learning.To analyze the impact of adaptation level, session progression, and taskconstraints on generalization success, a 2Ã2Ã2 mixed model ANOVA wasconducted. The data were assessed for compatibility with statistical as-sumptions. Out of the eight data sets, only three did not follow a normaldistribution. The excess kurtosis values for these three groups were -0.823,0.083, and 7.74. Given that a normal distribution has an excess kurtosisof zero, the violation was deemed minor except for one case, allowing us toproceed under the assumption of normality. Leveneâs test confirmed that alleight data sets satisfied the homogeneity of variances assumption.Table 2.2 presents the significance levels of all factors and their inter-actions for generalization performance. Consistent with task performance,232.7. ResultsSlow-adapters Fast-adapters Slow-adapters Fast-adapters00.20.40.60.81Success Rate***Session 1 Session 2High-constraint TaskLow-constraint TaskFigure 2.8: The average generalization success rate for both slow- and fast-adapters in the two sessions for the high- and low- constraint tasks.the results indicate a significant difference between slow- and fast-adapters,supporting H4. Additionally, Session-2 demonstrated a significantly highersuccess rate than Session-1, supporting H5.Examining individual trials across both sessions, we observed a similartrend to learned task performance, where slow-adapters required more trialsto reach high performance than fast-adapters, as shown in Figure 2.9. Inhigh-constraint tasks, slow-adapters initially exhibited a low success rate inSession-1 trials (0.51± 0.34) but gradually improved throughout Session-2,reaching (0.70 ± 0.15). Fast-adapters showed steady improvement in thefirst session and nearly converged to high performance by the sessionâs end(0.74 ± 0.21). In low-constraint tasks, slow-adapters achieved their highestperformance in the second trial of Session-2 (0.94±0.11), while fast-adaptersreached a higher performance (0.98± 0.06) by the second trial of Session-1.2.7.3 Mixed Quality Demonstrations AnalysisWe aim to investigate whether a large quantity of mixed-quality demonstra-tions can compensate for demonstration quality. This analysis highlightsthe importance of quantifying demonstration quality and ensuring that onlyhigh-quality demonstrations are used for robot learning. To explore this, wecompared task performance when learning from mixed-quality demonstra-tions versus learning from only high-quality demonstrations. This analysisfocused on slow-adapters, as they provided a broader range of mixed-qualitydemonstrations.242.8. DiscussionSlow-adapters Fast-adapters Slow-adapters Fast-adapters00.20.40.60.81Success RateLow-constraint Task High-constraint TaskFigure 2.9: The success rate of task generalization for both slow- and fast-adapters in all trials. Blue bars represent Session-1 trials and red barsrepresent Session-2 trials.For this evaluation, we combined all demonstrations from the three tri-als of the first session, resulting in a total of 27 demonstrations per session.A TP-GMM was trained using these mixed-quality demonstrations, and itstask performance and generalization ability were assessed. This performancewas then compared to a model trained on only nine high-quality demonstra-tions (one trial).The results indicate that using just one trial of high-quality demonstra-tions led to a 14% higher performance compared to using 27 mixed-qualitydemonstrations from both sessions in the high-constraint task. These find-ings emphasize the negative impact of low-quality demonstrations on learn-ing and generalization, reinforcing the importance of assessing demonstra-tion quality and prioritizing high-quality data in Learning from Demonstra-tion (LfD).2.8 DiscussionThe results show a strong correlation between task performance and gener-alization. Additionally, there is a clear distinction between fast- and slow-adapters in both task performance and generalization. However, with prac-tice, slow-adapters significantly improve, eventually approaching the perfor-mance levels of fast-adapters.The categorization of users into fast- and slow-adapters suggests that252.8. DiscussionTable 2.2: 2x2x2 mixed model ANOVA results of task generalization successrateFactor(s) F p-value Levels Marginal MeansSessions 14.93 p < 0.001Session-1 0.69± 0.025Session-2 0.79± 0.014Adaptation Level 2402.87 p < 0.001Slow 0.68± 0.020Fast 0.80± 0.023Task Constraints 48.36 p < 0.001Low 0.86± 0.02High 0.62± 0.026self-reported robotics expertise does not necessarily correlate with task per-formance and generalization ability. Prior expertise in domains outsiderobotics may influence adaptation to the task [66]. This indicates that re-lying solely on self-reported expertise may be insufficient, as transferableskills from other domains can contribute to usersâ teaching performance andadaptation [67].The results provide strong support for H1, demonstrating a significantcorrelation between task performance and generalization. Task learningresults further support H2, showing a significant main effect of adapta-tion level on success rate. Additionally, H3 is supported, as slow-adaptersexhibited a 43% improvement in success rate from Session-1 to Session-2,whereas fast-adapters improved by only 11%. The importance of practicefor slow-adapters is further highlighted by the significant difference betweenfast- and slow-adapters in Session-1, with an estimated mean difference of(0.21± 0.055). However, this difference was no longer significant in Session-2, where the mean difference decreased to (0.04 ± 0.035). These findingsalign with Thorndike et al. [80], who proposed that individual differencesin learning could be determined by assessing whether individuals convergeor diverge after practice. If individuals become more similar with practice,early performance differences are likely attributable to previously acquiredlearning skills.Another key finding is that the performance improvement between thetwo sessions is twice as large for the high-constraint task compared to thelow-constraint task. This suggests that there is greater room for improve-ment in more complex tasks, whereas simpler tasks may require less prac-tice. This highlights the need to account for task complexity when designingtraining programs for slow-adapters before they engage in robot teaching by262.8. Discussiondemonstration.Task generalization results follow a similar pattern to task learning,providing support for H4 and partial support for H5. Although the in-teraction between session and adaptation level was not statistically sig-nificant, the estimated marginal means indicate a 25% improvement fromSession-1 to Session-2. Additionally, the difference between fast- and slow-adapters in Session-1 was (0.19±0.049), whereas this difference decreased to(0.08 ± 0.028) in Session-2. Unlike task learning results, generalization im-provements were higher in the low-constraint task (0.12± 0.032) comparedto the high-constraint task (0.09± 0.047). This suggests that generalizationin constrained environments is more challenging, particularly for reachingdistant targets on the boxâs face.Analyzing performance at the individual trial level (Figure 2.7 and Fig-ure 2.9), we found that both fast- and slow-adapters exhibit low perfor-mance with high variance in their very first trial. This suggests that ini-tial demonstrations should not be used for robot learning, regardless ofthe userâs stated expertise, aligning with findings from [58]. Furthermore,fast-adapters quickly achieve their highest performance after the first trial,whereas slow-adapters require more time. This suggests that fast-adaptersmay need three trials or fewer to achieve high-quality demonstrations, whileslow-adapters may require at least six trials. Interestingly, participantsâperformance improved across trials despite receiving no explicit feedback ontheir performance. Additionally, some participants independently identifiedcommon issues in their demonstrations, such as unnecessary motions [48]and joint limit violations [70], and subsequently corrected these errors inlater trials.These findings can be interpreted in light of the motor skill learning lit-erature. Fitts and Posner [81] describe a three-stage model for motor skillacquisition: the cognitive stage, the associative stage, and the autonomousstage. In the cognitive stage, individuals process information about the taskand experiment with different strategies, resulting in high cognitive load andvariable performance. Upon finding a suitable strategy, they transition tothe associative stage, where they refine their approach to improve perfor-mance. Finally, in the autonomous stage, they can execute the task withhigh accuracy and minimal cognitive effort. Our results suggest that fast-adapters progress more rapidly through these stages, whereas slow-adaptersrequire additional practice to reach the associative and autonomous stages.272.9. Chapter Summary2.9 Chapter SummaryIn this chapter, we proposed an approach for defining demonstration qualitybased on task learning and generalization performance. This approach wasvalidated in a generic learning task with two levels of constraints. Demon-stration data were collected over two sessions on different days to examineimprovements in robot teaching skills for both slow- and fast-adapters. Theresults indicate a significant correlation between task performance and gen-eralization performance. Additionally, a notable distinction was observedbetween slow- and fast-adapters across both task constraints.The findings further reveal that slow-adapters exhibit substantial im-provement from the first to the second session, with their performance ap-proaching that of fast-adapters by the second session. Fast-adapters, on theother hand, demonstrated consistent performance across both sessions, asthey were able to define an effective strategy for approaching the task earlierthan slow-adapters. These results align with the motor skill learning liter-ature, reinforcing the importance of practice and adaptation in improvingteaching effectiveness.Despite its promising results, the proposed approach has some limita-tions. Most notably, it relies on the performance of the learning algorithmas a proxy for demonstration quality. While this outcome-based measuregrounds the assessment in learning effectiveness, it also assumes that the al-gorithm is sufficiently trained to distinguish high- from low-quality demon-strations. This dependency may introduce bias or limit generalizability ifthe algorithm underfits or overfits the demonstration data. Additionally, theexperiments were conducted in a relatively narrow task domain over a shorttime frame, leaving open questions about how the approach generalizes tomore complex, multi-stage tasks or to other data modalities such as visionor force sensing. Finally, the approach currently evaluates demonstrationquality after the fact and does not yet provide real-time feedback to usersduring data collection, which is critical for supporting novice teachers inpractical settings.Building on these findings, several avenues for future work emerge. Theproposed approach can be integrated into active learning paradigms, whereusers are prompted to provide additional demonstrations if their initialdemonstrations are of low quality, thereby enhancing robot learning and gen-eralization. Additionally, incorporating user adaptation levels into trainingdesign could help determine the appropriate duration for training and prac-tice, making the process adaptable and personalized for each user. This ap-proach would improve overall efficiency by preventing unnecessary training282.9. Chapter Summarywhile ensuring that users provide high-quality demonstrations. Moreover,training strategies could be further adapted to match the complexity andnature of the task, allowing for more effective guidance tailored to differ-ent learning scenarios. Another valuable avenue for future exploration is adeeper investigation into the key properties of high- and low-quality demon-strations, further refining our understanding of what makes demonstrationseffective in robot learning.29Chapter 3Consistency Matters:Defining DemonstrationData Quality Metrics inRobot Learning fromDemonstration33.1 IntroductionThe previous chapter established a method for assessing demonstration qual-ity based on task performance and generalization, this chapter delves deeperinto understanding what constitutes a high-quality demonstration for robotlearning. Instead of relying on learning outcomes to evaluate demonstra-tions, we propose a method to assess demonstration consistency before feed-ing them into the learning algorithm. The motivation behind this shift is toprovide a way to assess demonstration quality without requiring learning-based validation, reducing the computational burden and allowing early in-tervention when low-quality demonstrations are detected.Prior work has explored various methods to define demonstration qual-ity [82, 83]. Some studies rely on heuristic quality indicators, such as unde-sired motions [18], variations in trajectory length and amplitude [84], distri-bution shifts [85], or ambiguous demonstrations [13]. However, these notionsof quality are often loosely defined and incomplete, lacking well-establishedmetrics to determine what constitutes a âbetterâ demonstration or to sys-tematically detect imperfections. The absence of systematic and coherentquality metrics poses challenges for both researchers and practitioners, as in-3This chapter is published inMaram Sakr, Juyan Zhang, H.F. Machiel Van der Loos,Dana Kulic´, and Elizabeth Croft. âConsistency Matters: Defining Demonstration DataQuality Metrics in Robot Learning from Demonstrationâ, ACM Transactions on Human-Robot Interaction, 2024 (in press).303.2. Related Workconsistent or suboptimal demonstrations can degrade learning performanceand hinder generalization [5, 18].Data quality can be assessed across various dimensions, each representinga measurable property of the data that influences its overall usefulness. Thisaligns with the broader definition of data quality in the ISO/IEC 25012standard [86], which is defined as âthe degree to which a set of characteristicsof the data meets the requirementsâ. Examples of such characteristics inthe machine learning literature include accuracy, completeness, consistency,and timeliness [87, 88]. Nevertheless, these general characteristics need tobe adapted with domain-specific metrics and methods for measurement,particularly for LfD tasks. Questions arise, such as: How is accuracy definedin demonstrations? What constitutes the consistency of the demonstrations?Our approach, as shown in Figure 3.1, directly addresses this need byintroducing a comprehensive set of metrics for defining and quantifying con-sistency in demonstration data, across both Cartesian and joint-space char-acteristics. By rigorously quantifying data quality in this way, we can ensurethat only high-quality demonstrations feed into learning models, enhancingthe predictive accuracy of both learning and generalization performance.This approach is designed to be applicable across diverse LfD scenarios,making it a practical tool for both researchers and practitioners. By vali-dating demonstration consistency as a key data quality component, we pro-pose a new approach for evaluating and curating data before model training,bridging a significant gap in LfD research and offering a scalable solutionfor improving robot learning outcomes.3.2 Related WorkObtaining optimal demonstrations for all tasks that a robot must learnis a significant challenge [10]. Identifying experts proficient in both thetask domain and in robotics to ensure high-quality demonstrations tailoredto task goals and the robotâs constraints is difficult [18]. Consequently,mixed-quality demonstrations are commonly used in learning. Knowing thequality of demonstrations beforehand allows lower-quality data to be filteredout, while multimodal learning approaches can be employed to focus on thehighest quality demonstrations [89â91].3.2.1 Learning from Mixed-Quality DemonstrationsExamining the effect of diverse-quality demonstrations, Mandlekar et al. [52]found that LfD models trained exclusively on high-quality demonstrations313.2. Related WorkFigure 3.1: Overview of the proposed approach. Data was collected fromusers with varying levels of expertise in robotics. The range of metrics listedin Table 3.1 was calculated to serve as feature inputs for the clusteringalgorithm. The data was then clustered into two groups: consistent andinconsistent. A learning model was subsequently trained using consistentand inconsistent data. Finally, performance was evaluated by calculatingthe success rate of the trained models.from a single human achieved higher success rates than models trained onlarger datasets from multiple demonstrators with varied levels of proficiency.Similarly, in Chapter 2, we showed that models trained on mixed-qualitydata perform worse than those trained on a smaller dataset of consistentlyhigh-quality demonstrations, underscoring the importance of ensuring high-quality data from each demonstrator before aggregation. This chapter buildson this foundation by independently evaluating the quality of demonstra-tions for each user, thereby enabling a more nuanced understanding of howdemonstration quality impacts robot learning and generalization.3.2.2 Metrics for Evaluating Demonstration QualityTask-Independent Quality MetricsThe literature presents multiple approaches to defining and assessing demon-stration quality, although few papers propose standardized or comprehensivemetrics. Chernova and Thomaz [9] suggested evaluating human input qual-ity based on factors such as task skill, time taken, and consistency; however,they did not provide specific metrics to measure these aspects. Kaiser etal. [48] identified three primary sources of sub-optimality in human demon-strations: unnecessary actions that do not aid the goal, incorrect actions323.2. Related Workthat reduce demonstration utility, and unmotivated actions influenced byhuman sensory information that is unavailable to the robot. While theyproposed methods to measure unnecessary actions via motion ratios anddiscontinuity checks, identifying incorrect actions remains challenging, anddistinguishing motivated from unmotivated actions often depends on thedemonstration method, such as teleoperation or kinesthetic teaching [23].Other studies focused on teaching efficacy and error detection withindemonstrations. Fischer et al. [70] identified common user errors, such as ex-cessive pressure on the gripper, proximity to singularities, and self-collisions,although they did not evaluate these errorsâ impact on robot learning andgeneralization. Sena and Howard [13] addressed data sparsity by definingtwo quality metrics: teaching efficacy, which measures task-space general-ization, and teaching efficiency, which normalizes efficacy by the numberof demonstrations. These metrics focus on optimizing demonstration quan-tity rather than assessing individual demonstration quality, which remainscritical for ensuring generalizable and reliable learning.Task-Dependent Quality MetricsResearchers have developed task-specific metrics for assessing demonstra-tion consistency. Ureche and Billard [69] proposed metrics for bimanualtasks, such as evaluating tool maneuverability, consistency across trials, andcoordination between arms. They measured consistency by tracking con-straint changes across each dimension, although performance tends to varyover time, leading to fluctuations in quality. This limitation points to theneed for trial-independent evaluations to capture high-quality demonstra-tions more effectively. Additionally, our work proposes an extensive set ofmotion features to determine which measures best predict learning and gen-eralization performance.Despite the recognized need for quality assessments, there is a notablelack of metrics and methods that comprehensively evaluate multiple dimen-sions of demonstration quality. This chapter addresses that gap by intro-ducing task-agnostic metrics to assess consistency in demonstration data,focusing on motion characteristics in both Cartesian and joint-space dimen-sions. To validate these metrics, we conducted two user studies, demon-strating that our consistency metrics reliably predict both task success andgeneralization performance. The results highlight the robustness of the met-rics in different tasks and robotic platforms. By applying these measuresto evaluate demonstrations before learning, we can ensure the inclusion ofonly high-quality data, ultimately enhancing the efficiency and effectiveness333.3. Proposed Approachof the Learning from Demonstration (LfD) process.3.3 Proposed ApproachWe begin by proposing a broad list of metrics to evaluate the consistency ofdemonstration data. These metrics evaluate various aspects of robot motionand investigate their influence on learning and generalization performance.These metrics consider both Cartesian and joint spaces for the robot. Ta-ble 3.1 shows the proposed metrics for evaluating the consistency of thedemonstration data. Path length metrics are selected to detect unnecessarymotions [18] that do not contribute to the taskâs goal. For instance, if a userstruggles to manoeuvre a robot toward the goal, the provided trajectorieswill be longer than those of a proficient user who manoeuvres the robotdirectly toward the goal. Additionally, trajectory smoothness improves mo-tion stability and smoothness in the learned and generalized trajectories [34].Fluent trajectories that avoid abrupt changes are also important for efficientnavigation, reducing wear and tear on robotic components, and minimizingenergy consumption [92]. Hence, we propose jerk, curvature, and effort aspotential metrics to quantify the quality and consistency of the demonstra-tions.The robotâs posture directly impacts reaching movements and manip-ulation tasks (e.g., pushing, pulling, reaching) [93]. In this context, ma-nipulability [94] serves as a kinematic descriptor indicating the ability toarbitrarily move in different directions of the task in a given joint configu-ration. Since the ability to manoeuvre the robot in the workspace degradesat singular configurations, this quality metric is important for allowing therobot to generalize the learned task across the workspace [71, 95]. In addi-tion to singularities, joint limits significantly affect the manoeuvrability ofthe robot in the workspace [96].If the robot operates in the same space as humans, it is important toconsider legibility as a quality metric [97]. Legibility measures how easilyan observer can infer a robotâs goal from its motion. It is defined basedon the probability assigned to the actual goal across the trajectory. Thisprobability is computed using the cost function for each potential goal, whichcombines the early differentiation of the trajectory and the progress towardsthe goal [98]. Trajectories with high legibility will allow an observer to inferthe goal early, given a small segment of the trajectory, and all points inthe trajectory will be directed toward that goal. Details on the legibilitycalculation are in Appendix B.343.4. Experimental DesignIt is worth noting that task goals are the primary objectives of thedemonstrator, with quality being a secondary concern. For instance, whenmanoeuvring a robot in a constrained space, avoiding collisions takes prece-dence over the robotâs manipulability or proximity to joint limits. Thus, itis essential to consider task and workspace constraints when applying thesemetrics. To reduce the dependency on task-specific constraints, we focuson the consistency of demonstrations in terms of these metrics rather thantheir absolute values.Consistency allows us to account for variations in task difficulty and en-vironmental constraints. Furthermore, consistent demonstrations indicatea proficient demonstrator who can repeatedly perform tasks efficiently andeffectively [69]. This proficiency is more informative for learning than ab-solute performance metrics [99], which can be skewed by a single good orbad demonstration. By quantifying the consistency of demonstrations, re-searchers can prioritize the most consistent ones for learning, ultimatelyensuring improved learning and generalization performance.3.4 Experimental DesignTo validate that the consistency of the proposed metrics is predictive ofdemonstration quality and improves learning performance (as in Figure 3.1),we collected demonstrations from users with diverse robotics experiencesusing kinesthetic teaching. The demonstrations were then clustered intoconsistent and inconsistent groups based on the proposed metrics. Finally,we evaluated the learning and generalization performance of both groups toexamine the correlation between the metrics and the robotâs performance.Kinesthetic teaching allows users to physically guide the robot to achievea task, with the robotâs state recorded via its onboard sensors (e.g., jointangles, torques) [5]. Recording demonstrations directly on the robot usingits integrated sensors eliminates the correspondence problem, which arisesfrom the mismatch between the human teacher and the robot learner dueto differences in sensing ability, body structure, and mechanics [9]. Weexperimentally validated the proposed approach across two different tasksand robots to demonstrate the generalizability of the proposed metrics.353.4. Experimental DesignTable 3.1: Proposed quality metrics. The variables denoted here are: x forend-effector position, q for joint angle, R for rotation matrix, x¨ for end-effector acceleration,...x for end-effector jerk,...q for joint jerk, Ï for torque,J for the Jacobian, nd for the total number of degrees of freedom (DOFs),qâd for the lower limit of the dth joint, q+d for the upper limit of the dthjoint. P (Gi|ξ1:t) is the posterior probability of goal Gi given the trajectorysegment ξ1:t.CriterionReferencenameFormulaPath Length in CartesianSpaceQxâTt=1 â¥xt â xtâ1â¥2Path Orientation Length QrotâTt=1 arccos(trace(Rt·RTtâ1)2)2Path Length in Joint Space QqâTt=1 â¥qt â qtâ1â¥2Jerk in Cartesian Space Q ...xâTt=1...x t2Jerk in Joint Space Q ...qâTt=1...q t2Manipulability QMâTt=1âdet(JtJTt )Distance to Joint Limit QqlimâTt=1ândd4(qdâqâd )(q+d âqd)(q+d âqâd )2Joint Effort QÏâTt=1ândd Ï2d,tCartesian Curvature QÎºâTt=1â¥xË(t)Ãx¨(t)â¥â¥xË(t)â¥3Legibility QL ââni=1 P (Gi|ξ1:t) logP (Gi|ξ1:t)3.4.1 Experiment 1: Button PressingWe used the data collected from the experiment described in Section 2.4.3.To capture variability in performance, we focused solely on the high-constrainttask data (as shown in Figure 3.2), as this task posed greater challengescompared to the low-constraint task. The low-constraint task exhibited rel-atively stable performance across trials, whereas the high-constraint taskdemonstrated greater variation, as illustrated in Figure 2.7.363.4. Experimental Design(a) Robotâs initial position (b) Struggling to manoeuvre around thebox(c) Finding a better way to manoeuvre (d) Pressing the buttonFigure 3.2: Button-pressing task demonstration overview from (a) the initialposition of the robot, (b) an example of a userâs struggle to get the robotaround the box, (c) an example of a better manoeuvre of the robot aroundthe box, and (d) the robot pressing the button.3.4.2 Experiment 2: Pick and PlaceExperimental Robot PlatformIn this experiment, we utilized the UR5 robotic arm from Universal Robots,a 6-degree-of-freedom (6-DoF) collaborative robot (cobot) known for its pre-cision, ease of use, and safety in human-robot interaction settings. The UR5has a payload capacity of 5 kg, a reach of 850 mm, and features force-torquesensing, allowing for compliant interaction, making it well-suited for LfDand kinesthetic teaching. To enable object manipulation, we equipped theUR5 with the Robotiq 2F-85 adaptive two-finger gripper, a versatile androbust end-effector with an 85 mm stroke and adjustable gripping force,allowing it to securely grasp a variety of objects.The robot system ran on ROS Melodic and allowed users to teach theUR5 arm through direct kinesthetic interaction. However, since the Robotiq2F-85 gripper didnât support kinesthetic control, we faced a small challenge373.4. Experimental Design(a) Robotâs initialposition(b) Picking up thebottle(c) manoeuvre therobot under theobstacle(d) Placing thebottle(e) Top view showing the pickupand place locations(f) The exper-imental bottlewith spillagelineFigure 3.3: Task demonstration overview: (a) Robotâs initial position, (b)Picking up the bottle, (c) Maneuvering under the obstacle, (d) Placing thebottle, (e) Top view of the pickup, obstacle, and placement locations, and(f) Experimental bottle with a spillage line. This figure provides an overviewof the experiment described in Section 3.4.2 and Section 4.4.1.in giving participants a seamless demonstration experience. To work aroundthis, we used a Wizard-of-Oz (WoZ) setup: participants simply said âopenâor âcloseâ to control the gripper, and the experimenter quietly executedthe action in real time. This gave participants the feeling of natural, voice-controlled interaction, without being distracted by interface limitations. Thecombination of the UR5 and the Robotiq 2F-85 gripper provided a reliablerobotic platform for our study, supporting both kinesthetic teaching andautonomous execution of demonstrated trajectories.Task DefinitionOur second example experimental task required participants to demonstratea pick-and-place operation involving a liquid-filled container while avoiding383.4. Experimental Designcollisions and spillage. This task did not require domain expertise but in-stead relied on iterative practice to produce smooth demonstrations fromwhich the robot could learn. Figure 3.3 featured a Universal Robots UR5equipped with a Robotiq 2F-85 two-finger gripper as the end-effector for ma-nipulation. Participants were instructed to pick up a bottle from one of fourpredefined pickup positions and place it at a designated location (referredto as the âplace locationâ) without spilling the liquid (Figure 3.3e). A fixedoverhead obstacle was positioned between the pickup and place locations,introducing an additional challenge for trajectory planning and execution.To facilitate spillage detection, the robot manipulated a sealed bottlewith visual landmarks (dotted lines above the liquid level) serving as spillageindicators (Figure 3.3f). A 2-cm margin was established between the liquidâsresting surface and the spillage threshold. Excessive end-effector orientationchanges, jerky movements, or high-speed actions could cause the liquid toexceed this threshold, triggering a spillage event.User StudyAt the beginning of the study, participants were informed that they wouldteach a robot a pick-and-place task using kinesthetic teaching, which in-volves physically guiding the robot to perform a task [17]. The experimenterdemonstrated how to correctly hold and guide the robot while it was ingravity compensation mode, ensuring participants understood the interac-tion method. Participants were introduced to the experimental setup andtask objectives and were instructed to provide smooth and natural demon-strations for the robot to learn from.Next, the experimenter explained the pick-and-place task, instructingparticipants to physically guide the robot while controlling the gripper usingvoice commands (âopenâ and âcloseâ). Participants were also instructed toavoid collisions with the obstacle and, if a collision occurred, to continuewhile attempting to minimize further collisions. Additionally, they wererequired to keep the liquid level as stable as possible, with spillage occurringif the liquid touched the threshold line. Once participants gripped the bottle,they were not allowed to release it until reaching the designated placementlocation. Safety instructions were then provided, highlighting pinch pointson the gripper and proper handling techniques. Participants were assuredthat the robot would remain stationary when not being guided.The baseline stage began with an assessment of participantsâ initial in-teraction with the robot and the task. Participants started with the bottlein the first location (shown in Figure 3.3f), and the robot was set to gravity393.4. Experimental Designcompensation mode, allowing them to physically guide the robot to pick upthe bottle and place it in the designated location. To control the gripper,participants gave voice commands such as âopenâ or âclose,â and the exper-imenter executed these commands on the computer to control the gripperusing WoZ. Once the bottle was placed, the data was saved as ROSbag filesfor offline analysis. The robot was then reset to the initial position (Fig-ure 3.3a), and the bottle was moved to the second location. This process wasrepeated until participants provided four demonstrations from each pickuplocation. The order of the pickup locations (as shown in Figure 3.3e) wasdesigned to progress from easy to hard; the closer the pickup location wasto the robot, the more challenging it became to position the robot to pickup the bottle. This phase was referred to as the baseline performance, asit captured participantsâ demonstrations during their first interaction withthe task.After this baseline phase, all participants underwent a practice periodusing one of the pickup locations (location 4), chosen for being the mostchallenging position. Each participant was assigned to one of three traininggroups (as described in Section 4.3), with equal practice time allotted foreach group. They were encouraged to experiment with different strategiesto develop a smooth approach to teaching the robot the task. Following thepractice session, a retention test identical to the baseline was conducted tocapture participantsâ performance after practice. For this test, participantsprovided four demonstrations from each location, picking up the bottle andplacing it in the designated spot to assess their performance post-practice.ParticipantsWe recruited participants for our user study through word of mouth, ad-vertisements on the university campus, and social media. A total of 30participants (25 male, 5 female) with an average age of 27 years took part inthe study. All but two participants were right-handed. The participants hadvarying levels of expertise in robotics, with 43% reporting no prior formaltraining in robotics and no previous physical interaction with a robot. An in-formed consent was obtained from each participant before the experimentalsessions.403.4. Experimental Design3.4.3 Evaluation of Learning and GeneralizationPerformanceA TP-GMM model (as described in Appendix A) was trained for each par-ticipantâs data in each task. The model was then evaluated at the samedemonstration location, referred to as task performance, and at novel loca-tions, referred to as generalized performance.Experiment 1: Button-pressing task evaluationThe model for this task was evaluated as described in Section 2.5.1 andFigure 2.4.Experiment 2: Pick-and-place task evaluationModel performance was evaluated by generating trajectories to pick andplace the bottle from the same demonstrated locations (task performance),as in Figure 3.4a, and from new pickup locations (generalized performance),as in Figure 3.4b. The generalized pickup locations were created by discretiz-ing the pickup space into a 4x4 grid, resulting in 16 new pickup locations.The grid size was chosen based on the UR5 robotâs reachability space andthe diameter of the experimental bottle (7 cm).The learned trajectory was considered successful if the bottle was pickedup and placed without âspillageâ. Spillage was detected by monitoring de-viations in upright orientation, acceleration, and jerk, which serve as prox-ies for potential liquid movement. We normalized the orientation deviation,jerk, and acceleration values to assess spillage risk. As the jerk, acceleration,and orientation deviation values approach zero, the success score approaches1, provided the bottle was picked up and placed correctly. However, as jerk,acceleration, and/or orientation deviation increase, the success score is pe-nalized accordingly. The Overall Success was computed using a weightedsum of the pick-and-place success and the normalized spillage proxies, asshown in Equation 3.1.Soverall =ï£±ï£²ï£³0, pickup failure12(Sp + Sl) +12 Ã (1âJn)+(1âAn)+(1âOn)3 , pickup successful(3.1)where Sp denotes the pickup success, with Sp = 0.5 if the bottle was success-fully picked up and Sp = 0 otherwise. Sl represents the place success, with413.5. Hypotheses(a) Same demonstrated states (b) Generalized statesFigure 3.4: The task simulation includes both (a) the same demonstratedstates and (b) the generalized states used to evaluate model performance.The blue segment of the trajectory represents the movement from the ini-tial position to the pickup location, while the green segment represents themovement from the pickup to the place location.Sl = 0.5 if the bottle was placed correctly and Sl = 0 otherwise. The valuesJn, An, and On correspond to the normalized metrics for jerk, acceleration,and orientation deviation, respectively,The Phase Success score was then calculated by averaging the overallsuccess rates across all tested locations, as defined in Equation 3.2. In thetask performance test, this phase success was calculated across four loca-tions, while in the generalization test, it was calculated across 16 locations.Phase Success =âNi=1Overall SuccessiN(3.2)where Phase Success represents the average success score across all testedlocations within the phase. Overall Successiis the individual success scorefor each location i, calculated as in Equation 3.1. N denotes the totalnumber of tested locations.3.5 HypothesesWe expect that the demonstrationsâ consistency in terms of the proposedmeasures is a key identifier of the quality, and consequently, of task perfor-mance and generalized performance. Moreover, we believe that practice iscrucial for improving the quality of demonstrations, which in turn enhances423.6. Resultstask performance and generalized performance. Based on these expecta-tions, we formulate the following hypotheses:â¢ H1-a: The consistency of demonstrations in terms of the proposedmetrics significantly contributes to the task performance success rate.â¢ H1-b: The consistency of demonstrations in terms of the proposedmetrics significantly contributes to the generalized performance suc-cess rate.â¢ H2: Participantsâ practice significantly improves the consistency ofdemonstrations.â¢ H3-a: Participantsâ practice significantly improves the task perfor-mance success rate.â¢ H3-b: Participantsâ practice significantly improves the generalizedperformance success rate.3.6 ResultsTo evaluate our hypotheses, we assessed the consistency of the demonstra-tions provided by each participant. We employed a K-means clusteringalgorithm [100] to categorize each set of demonstrations into one of twoclusters: a consistent cluster and an inconsistent cluster, as shown inFigure 3.1. For each trial in the button-pressing experiment, the range ofthe ten metrics listed in Table 3.1 was calculated and used as input for theclustering algorithm (multi-dimensional clustering). The rationale for usingthe range is that consistent demonstrations are expected to exhibit smallervariations in metric values, while inconsistent demonstrations are expectedto show larger variations. Prior to clustering, all metric range values werestandardized using z-score normalization across participants. This prepro-cessing step ensured that all features were unit-invariant and contributedequally to the clustering process, regardless of their original scale (e.g., cen-timetres, radians, torque units). Since ranges are positive measures of vari-ability, the orientation (positive or negative association with success) of eachmetric does not impact clustering outcomes. K-means clustering was thenperformed using squared Euclidean distance, which is the standard distancemetric in K-means.Figure 3.5a shows the standardized metric values for both the consis-tent and inconsistent clusters in the button-pressing task. The inconsistent433.6. Resultscluster displayed a larger range across most metrics, while the consistentcluster exhibited a smaller range. Specifically, the inconsistent cluster hada broader distribution in all pathlength, jerk, and effort metrics. In con-trast, the distribution for distance to joint limits, curvature, and legibilitywas similar between the two clusters. Additionally, the inconsistent clustershowed higher median values for pathlength, jerk, manipulability, and effortmetrics, indicating that demonstrations in the inconsistent trials involvedmore variability, longer paths, greater jerk, more singular poses, and higherenergy expenditure. Conversely, the consistent trials, with lower medianvalues for these metrics, demonstrated smoother, more conservative, andless energy-intensive movements.In the pick-and-place experiment, we applied the same approach by us-ing the range of the proposed metrics from each participantâs baseline andretention phases as input features for the K-means clustering algorithm. Un-like the button-pressing experiment, legibility was excluded here since therewas only one goal (placement) location. Figure 3.5b shows the standardizedmetric values for both clusters. Similar to the button-pressing experiment,the inconsistent cluster had a higher range for most metrics compared to theconsistent cluster, which had a smaller range. Moreover, the distribution ofthe range for pathlength, jerk, curvature, and effort metrics differed signif-icantly between the two clusters, while the distribution for manipulabilityand distance to joint limits was more similar between them. The averagerange values for each metric within the consistent and inconsistent clustersfor both experiments are summarized in Appendix C.1, offering additionalguidance for future applications of this clustering approach.3.6.1 Consistency impact on the task and generalizedsuccess rateAfter categorizing the consistent and inconsistent clusters, we investigatedthe task performance success rate for both clusters. As shown in Figure 3.6,the task performance success rate when training the model with consistenttrials is higher than when training with inconsistent ones. For the button-pressing experiment results, a one-way ANOVA was performed to investigatethe impact of consistency on the success rate. Since the number of inconsis-tent sets in each trial is low, we aggregated the inconsistent and consistentsets across all trials. This analysis yields the relationship between the twovectors: one for the task success rate and the other for the consistency label(0 or 1). We found that the consistent group had a significantly higher suc-cess rate than the inconsistent group (F (1, 142) = 45.46, p < 0.001), with an443.6. Resultsaverage success rate of (0.76±0.02)4 in the consistent group and (0.24±0.08)in the inconsistent group. Similarly, a one-way ANOVA was performed onthe data from the pick-and-place experiment. We found that the consistentgroup had a significantly higher success rate than the inconsistent group(F (1, 58) = 44.01, p < 0.001) with an average success rate of (0.79 ± 0.03)in the consistent group and (0.25 ± 0.11) in the inconsistent group. Theseresults support H1-a.We conducted the same ANOVA analysis on the generalized performancesuccess rate and found a similar result, as shown in Figure 3.7. In the button-pressing experiment, the consistent group had a significantly higher successrate than the inconsistent group (F (1, 143) = 36.57, p < 0.001), with anaverage success rate of (0.63±0.02) in the consistent group and (0.22±0.06)in the inconsistent group. Similarly, in the pick-and-place experiment, theconsistent group had a significantly higher success rate than the inconsistentgroup (F (1, 58) = 15.50, p < 0.001), with an average success rate of (0.81±0.03) in the consistent group and (0.49 ± 0.10) in the inconsistent group,supporting H1-b.3.6.2 Impact of practice on demonstration consistencyTo assess H2 and investigate the impact of practice on the consistency ofthe demonstrations, a MANOVA (Multivariate Analysis of Variance) wasperformed to investigate the impact of practice on the range of the tenmetrics, as detailed in Table 3.2. In the button-pressing experiment, themultivariate tests revealed a significant effect of practice (trials) on the con-sistency of the demonstrations (Royâ²sLargestRoot = 0.559, F (10, 110) =6.144, p < 0.001, η2p = 0.358), with 35.8% of the variance in the range ofmetrics explained by the effect of the trial. Moreover, Figure 3.6a showsthat the number of inconsistent trials that are also first trials is higher com-pared to later trials, indicating that consistency improves with practice.Similarly, we conducted MANOVA on the range of nine metrics of the pick-and-place experiment data to investigate the practice/training impact on theconsistency of these metrics. The test revealed a significant effect of prac-tice/training on the consistency of the demonstrations (Royâ²sLargestRoot =0.559, F (9, 21) = 4.35, p < 0.005, η2p = 0.651), with 65.1% of the variance inthe range of metrics explained by the effect of practice, supporting H2.Univariate tests on the button-pressing experiment showed significanteffects of the trials on the following range of metrics: jerk in Cartesian space,4(mean± SE)453.6. ResultsTable 3.2: Results of MANOVA and Univariate Tests for Button-Pressingand Pick-and-Place ExperimentsMANOVA ResultsExperiment Royâs Largest Root F p-value η2pButton-Pressing 0.559 6.144 0.001 0.358Pick-and-Place 0.559 4.350 0.003 0.651Univariate Test ResultsMetric Experiment F p-value η2pPath Orientation Length Button-Pressing 2.40 0.04 0.10Jerk in Cartesian Space Button-Pressing 4.12 0.002 0.15Effort Button-Pressing 3.07 0.012 0.12Pathlength in Cartesian Space Button-Pressing 2.10 0.07 0.08Pathlength in Joint Space Button-Pressing 2.10 0.07 0.08Manipulability Button-Pressing 2.12 0.07 0.08Pathlength in Cartesian Space Pick-and-Place 13.23 0.001 0.31Pathlength in Joint Space Pick-and-Place 7.90 0.009 0.21Path Orientation Length Pick-and-Place 9.52 0.004 0.25Jerk in Cartesian Space Pick-and-Place 12.51 0.001 0.30Jerk in Joint Space Pick-and-Place 7.87 0.009 0.21path orientation length, and effort (p < 0.05). They also revealed marginallysignificant effects of the trials on the range of metrics for pathlength inCartesian space, pathlength in joint space, and manipulability (p = 0.07).Post hoc pairwise comparisons revealed that participants may need moretime to achieve significantly better values for some metrics than others. Forexample, the participants showed a significant improvement after one trialin terms of pathlength in Cartesian and joint space, manipulability, andcurvature. For metrics like jerk in Cartesian and joint space and effort,two trials were required for participants to better demonstrate the task andachieve significantly lower jerk values. Additionally, for legibility, only thefirst and last trials were significantly different, indicating that participantsmay need more time to generate legible demonstrations. Similarly, in thepick-and-place experiment, univariate tests showed a significant effect of thepractice on the range of the following metrics: pathlength in Cartesian andjoint space, orientation pathlength and jerk in Cartesian and joint space(p < 0.01).463.6. Results(a) First experiment (button-pressing task with PR2)(b) Second experiment (pick-and-place task with UR5)Figure 3.5: The standardized feature values in the resulting two clusters ofdemonstrations. The first cluster (blue) includes consistent demonstrationsand the second cluster (orange) includes inconsistent demonstrations.473.6. Results(a) First experiment (button-pressingtask with PR2)(b) Second experiment (pick and placetask with UR5)Figure 3.6: Average task performance success rate across trials/phases forboth consistent and inconsistent groups.(a) First experiment (button-pressingtask with PR2)(b) Second experiment(pick and placetask with UR5)Figure 3.7: Average generalized performance success rate across tri-als/phases for both consistent and inconsistent groups.483.6. Results3.6.3 Practice impact on the task and generalized successrateWe hypothesized that practice improves the overall success rate for both taskperformance and generalized performance. This is because the consistency(as shown earlier) and other factors improve over time. To evaluate this hy-pothesis (H3-a and H3-b), a repeated ANOVA with a Greenhouse-Geissercorrection was performed to account for the violation of the sphericity as-sumption, exploring the impact of practice (trials) on the success rate oftask performance, as shown in Table 3.3. We found that practice has a sig-nificant impact on the success rate (F (1, 23) = 23.47, p < 0.001, η2p = 0.51).The success rate improved from (0.52±0.07) in the first trial to (0.85±0.05)in the last trial. In addition, the post hoc pairwise comparison of the trialsshows that trials 1, 2, and 3 have significantly lower success rates than tri-als 4, 5, and 6, while there is no significant difference within the first threetrials or within the last three trials. This highlights that the significantimprovement is revealed in the second session on later days, which agreeswith Walker et al. [75]. The same analysis was applied to the pick-and-placeexperiment task success rate. The repeated ANOVA showed task successrate in retention (after training) is significantly higher than the success ratein the baseline (before training) (F (1, 29) = 9.09, p < 0.01, η2p = 0.24) withan average success rate of (0.66± 0.06) in the baseline and (0.80± 0.03) inthe retention, supporting H3-a.A repeated ANOVA was conducted on the generalized success rate (asdetailed in Table 3.4) and we found a similar result. In the button-pressingexperiment, practice has a significant impact on the generalized success rate(F (1, 23) = 6.16, p < 0.05, η2p = 0.21). The success rate improved from(0.51±0.06) in the first trial to (0.70±0.03) in the last trial. In the pick-and-place experiment, training has a significant impact on the generalized successrate (F (1, 29) = 5.87, p < 0.05, η2p = 0.17). The success rate improvedfrom (0.73 ± 0.05) in the baseline(before training) to (0.82 ± 0.03) in theretention(after training), supporting H3-b.The post hoc pairwise comparison of the trials in the button-pressingexperiment shows that trials 1 and 2 are significantly less successful thantrial 6, while there is no significant difference among the intermediate trials.This indicates that improvement in the generalized success rate is slowerthan in the task performance success rate, and more time is needed for theparticipants to achieve a good quality of demonstrations that attain a bettergeneralized success rate.493.6. ResultsTable 3.3: Repeated ANOVA Results for Task Performance Success RatesExperiment Phases/Trials F p-value η2pButton-Pressing 6 Trials 23.47 < 0.001 0.51Pick-and-Place 2 Phases 9.09 < 0.01 0.24Trial/Phase Button-Pressing Pick-and-PlaceInitial (First Trial/Baseline) 0.52± 0.07 0.66± 0.06Final (Last Trial/Retention) 0.85± 0.05 0.80± 0.03Table 3.4: Repeated ANOVA Results for Generalized Success RatesExperiment Phases/Trials F p-value η2pButton-Pressing 6 Trials 6.16 < 0.05 0.21Pick-and-Place 2 Phases 5.87 < 0.05 0.17Trial/Phase Button-Pressing Pick-and-PlaceInitial (First Trial/Baseline) 0.51± 0.06 0.73± 0.05Final (Last Trial/Retention) 0.70± 0.03 0.82± 0.033.6.4 The importance of each metric to the task successrateThus far, we have demonstrated that consistency is critical for achiev-ing higher success rates and that practice improves demonstration quality,which, in turn, enhances success rates. We have also observed that theproposed metrics show different patterns across trials and clusters. In thissection, we explore the importance of each metric in determining successrates by investigating the question of What are the key aspects to evaluatein demonstrations before learning?To address this, we conducted a correlation analysis between the tasksuccess rate and the range of ten proposed metrics. Additionally, we includeda binary parameter, consistency, to examine its contribution to the successrate. Figure 3.8 illustrates a heatmap showing the correlations between themetrics and the success rate in both experiments. In both experiments,pathlength, jerk, and consistency exhibit the highest correlations with tasksuccess rate. Specifically, pathlength and jerk have negative correlations503.6. Resultswith success rate, indicating that greater variability reduces success, whileconsistency shows a positive correlation, suggesting that more consistentdemonstrations improve performance.Other terms vary between experiments. Effort has a high correlationwith success rate in the button-pressing experiment but a lower correlationin the pick-and-place. Conversely, the distance to joint limits correlates morestrongly with success rate in the pick-and-place experiment. We also foundthat some metrics, like pathlength, are highly correlated with each other,and consistency correlates strongly with pathlength, jerk in joint space, andeffort. This pattern aligns with Figure 3.5, where pathlength, jerk, andeffort show the most distinct patterns between clusters.Motivated by these correlations, we performed multiple regression anal-ysis, including interaction terms, to examine how changes in correlated met-rics affect the success rate. The range of the proposed metrics collectivelypredicts 70.2% and 88.6% of the task success rate in the two experiments,respectively. Notably, the interactions among the ranges of pathlength andjerk metrics are the most significant factors with high coefficients for es-timating the success rate in both experiments. Details of the regressionmodels and the analysis are provided in Appendix E.1.513.6. Results(a) First experiment (button-pressing task with PR2)(b) Second experiment (pick and place task with UR5)Figure 3.8: Heatmap shows the correlation matrix of the task success rate,and the range of the metrics and the consistency. The metrics are definedin Table 3.1523.6. Results(a) First experiment (button-pressing task with PR2)(b) Second experiment (pick and place task with UR5)Figure 3.9: Heatmap shows the correlation matrix of the generalized successrate, and the range of the metrics and the consistency. The metrics aredefined in Table 3.1533.7. Discussion3.6.5 The importance of each metric to the generalizedsuccess rateIn this section, we examine the relationship between the generalized successrate and demonstration consistency using the proposed measures. Similarto the analysis of the task success rate, we first conducted a correlationanalysis. Figure 3.9 presents a heatmap showing the correlation betweenthe generalized success rate, the range of the proposed measures, and thebinary consistency variable across the two experiments. Pathlength, jerk,and consistency measures exhibit the highest correlation with the general-ized success rate, mirroring the pattern observed for task success. Simi-larly, effort shows a high correlation with the generalized success rate in thebutton-pressing experiment but a weaker correlation in the pick-and-placeexperiment. Conversely, distance to joint limits has a stronger correlationwith the generalized success rate in the pick-and-place experiment than inthe button-pressing. Several measures are also strongly correlated with oneanother, which motivated the use of regression analysis with interactionterms to better understand their combined impact on success.The regression models indicate that the range of the proposed metricscollectively predicts 75.6% and 90.7% of the generalized success rate in thetwo experiments, respectively. Interactions among the pathlength and jerkmetrics are the most significant factors for estimating the generalized successrate in both experiments, as observed in the task success rate models. Detailsof the regression models and the analysis are provided in Appendix E.2.3.7 DiscussionIn this chapter, we proposed a set of metrics to evaluate the quality ofdemonstrations prior to learning from demonstration (LfD). Specifically, wefocused on consistency as a key measure of data quality and explored variousmotion characteristics to identify which metrics best predict learning perfor-mance. Unlike previous studies that artificially inject noise into data [32], weevaluated our approach using real demonstration data collected from userswith varying levels of robotics expertise. The experimental results clearlydemonstrate that consistency in demonstrations significantly impacts boththe success rate of learning and its generalization in different robot tasks.Our clustering analysis revealed that inconsistent demonstrations arecharacterized by larger ranges and higher means in path length, jerk, andeffort. In contrast, consistent demonstrations exhibit smaller ranges in thesemetrics, indicating smoother, more conservative, and less energy-intensive543.7. Discussionmovements. This finding supports the notion that smoother and more con-trolled demonstrations provide better input for learning algorithms, reducingthe likelihood of overfitting to noisy or suboptimal data.Statistical analyses further highlight the significant contribution of con-sistency to both task success rates and generalization performance. Thesefindings align with Pomerleauâs work [101], where consistent data providedclearer, more repeatable patterns for learning algorithms, leading to im-proved performance. Importantly, we observed that practice leads to sig-nificant improvements in consistency. However, the rate of improvementvaried across metrics. For example, path length (in both Cartesian andjoint spaces), manipulability, and curvature improved significantly after justone trial, whereas metrics like jerk, legibility, and effort required more prac-tice to show meaningful improvement. This suggests that while some as-pects of demonstrations can be refined quickly, others take time and experi-ence to reach optimal levels. This opens up an exciting direction for futurework, such as developing personalized training methods that track and ad-just for individual user progress across specific metrics. By monitoring thesemetrics, we could avoid overtraining, which might otherwise diminish userperformance, and ensure the highest quality demonstrations from each par-ticipant. Researchers have attempted to implement training curricula forusers [63, 102, 103], but they did not provide a way to evaluate performanceover trials, which the proposed metrics would help in achieving.All of the proposed metrics can be computed during data collection,prior to running the learning algorithm, with the exception of legibility,which requires prior knowledge of task goals. Legibility becomes especiallyimportant in human-robot interaction scenarios, where the robotâs actionsneed to be easily understood by humans [97]. Thus, while legibility may notbe necessary in all learning tasks, it plays a critical role in ensuring intuitiveand seamless interaction in shared environments.The correlation and regression analyses provided further insights into themetrics that are most influential in achieving high task success and gener-alization rates. They revealed that all metrics significantly impact the tasklearning and generalization success rate, either directly or through interac-tion with each other. However, the nature of the task influences the relativeimportance of each metric. For example, in the first experiment (button-pressing task in a constrained space), minimizing pathlength in joint spacewas essential to avoid self-collision and collision with the experimental setup.Orientation also played a key role in the button-pressing task, as the angleof approach was critical to successful execution. Interestingly, effort andjerk were highly correlated with pathlength and orientation, as shown in553.7. DiscussionFigure 3.8. Thus, their interactions have a high impact on the success rate,as shown in Table E.1. Notably, even though these metrics had the high-est impact, the influence of the other metrics cannot be ignored. When wereran the regression using only these four metrics and ignored the others,only 45% of the variance in the task success rate was captured, highlightingthe importance of a comprehensive evaluation. These findings suggest thatresearchers should prioritize the selection of the most consistent demonstra-tions across all metrics to achieve high performance in both learning andgeneralization.In the second experiment (pick-and-place task), pathlength and jerkagain emerged as critical metrics for both task and generalization success.The consistency of these findings across experiments highlights the robust-ness of these metrics in predicting learning performance. However, we alsoobserved differences in the role of other metrics. For instance, the rangeof effort was more highly negatively correlated with success in the first ex-periment than the second one. Conversely, the range of distance to jointlimits had a stronger negative correlation with success in the second ex-periment. These findings suggest that the specific nature of the task andits constraints influence which metrics are most important for optimizinglearning and generalization.Although we employ TP-GMM in our experiments, our consistency met-rics are algorithm-agnostic and not specific to any one LfD technique. TP-GMM was selected due to its interpretability, low sample complexity, andsuitability for structured, trajectory-based tasks. However, the core prin-ciple behind our metricsâcapturing variability in motion qualityâcan beapplied to a broad range of LfD methods. To this end, we provide a prelimi-nary evaluation using a diffusion-based learning model in the appendix C.2.While the diffusion model replicated our main findings regarding consistency,it also exhibited different generalization behavior compared to TP-GMM,underscoring the importance of evaluating consistency effects across diversemodeling paradigms. Future work will expand this analysis to include ad-ditional algorithms, such as Behavioral Cloning and adversarial imitationlearning, using larger and more diverse datasets. This direction is supportedby prior work in both imitation and supervised learning, which has shownthat the consistency and quality of training data significantly affect learningperformance regardless of the model used [18, 85, 104].Our proposed approach enables the evaluation of demonstration qualityand consistency without the need to train the learning model, which sets itapart from prior work [60, 105]. Moreover, the proposed metrics implicitlycapture inconsistencies in the solution strategy. For example, in the first563.8. Chapter Summaryexperiment, users initially pushed the robotâs shoulder joint to its limit toavoid collisions with a box. With practice, users discovered smoother andmore efficient ways of manoeuvring around the box, as seen in Figure 3.2c.This pattern places the first trial in the inconsistent group, while subsequenttrials fall into the consistent group. This suggests a promising direction forfuture work in active learning, where users could be guided to provide moreconsistent and higher-quality demonstrations, as explored in [105]. Whiletheir approach requires retraining the policy and evaluating performance tomeasure compatibility, our method reveals inconsistencies and offers insightsinto demonstration quality without the need for training, providing a moreefficient and practical approach.3.8 Chapter SummaryThis chapter presents an extensive set of metrics for evaluating the qual-ity of demonstrations in Learning from Demonstration (LfD) tasks, with aparticular focus on consistency as a key determinant of success. By rig-orously analyzing real demonstration data from users of varying expertise,we demonstrate that consistency significantly influences both task successand generalization performance in robot learning. Our findings show thatsmoother, more controlled demonstrationsâindicated by smaller ranges inpathlength and jerk in both Cartesian and joint spacesâlead to betterlearning outcomes. Furthermore, our approach enables real-time evaluationof demonstration quality, making it possible to assess consistency prior totraining and ensuring that only high-quality data is used for robot learning.Results from two user studies involving distinct tasks and robot plat-forms underline the robustness of the proposed metrics. Pathlength andjerk emerged as critical predictors of learning success across both exper-iments, highlighting their relevance for a wide range of LfD applications.Additionally, our work emphasizes the role of task-specific factors in shap-ing the contribution of each metric, suggesting that personalized trainingprotocols may further enhance demonstration quality.Overall, this work addresses a critical gap in LfD research by provid-ing a practical, data-driven method to assess demonstration quality beforelearning. It opens new avenues for optimizing robot learning and general-ization in real-world scenarios, empowering non-expert users to teach robotsmore effectively while minimizing the risks associated with suboptimal data.Future work will focus on integrating these metrics into adaptive trainingmethods and exploring active learning strategies to guide users in providing573.8. Chapter Summarymore consistent, higher-quality demonstrations.While the proposed consistency metrics demonstrated strong predictivepower and generalizability, several limitations highlight directions for fu-ture research. First, the metrics were primarily validated on short-durationtasks using kinematic data; extending them to complex, multi-stage tasksor additional modalities (e.g., vision or force) remains unexplored. Second,although the approach supports real-time evaluation, it does not yet accountfor task intent or semantic correctnessâfactors that could impact qualityassessment in more nuanced scenarios. Lastly, the current evaluation isat the trajectory level; future work could investigate intra-demonstrationtemporal dynamics and user-specific variability to further refine consistencyassessment.58Chapter 4Empowering Lay Users:Evaluating TrainingTechniques to ImproveRobot Teaching Skills54.1 IntroductionAfter establishing what constitutes high-quality demonstrations for robotlearning and generalization, this chapter proposes training methodologies tohelp novice users provide effective demonstrations. This work aligns withthe second objective of the thesis by focusing on improving user teachingskills through structured training approaches.Prior efforts in LfD have sought to simplify the process of robot teach-ing for novice users by introducing keyframe-based teaching [106] or break-ing down tasks into subcomponents using hierarchical task structures [107].However, defining key points of a task and abstracting a task into meaning-ful subcomponents remain challenging for novices, as they require guidanceto effectively structure their demonstrations [108]. Some studies have pro-vided explicit instructions on how to teach a robot each task [14], whileothers have emphasized the necessity of user training before they beginteaching [109]. However, existing methods often require extensive training,sometimes lasting several hours, making them impractical for real-world ap-plications. In contrast, this chapter introduces training methodologies thatefficiently transfer expert skills to novice users without requiring direct ex-pert supervision.Other approaches focus on designing teaching interfaces that make robot5This chapter will be submitted as Maram Sakr, Alexander Calvert, Rachel Love,H.F. Machiel Van der Loos, Dana Kulic´, and Elizabeth Croft. âEmpowering Lay Users:Evaluating Training Techniques to Improve Robot Teaching Skillsâ, to the ACM Transac-tions on Human-Robot Interaction.594.2. Related Workprogramming more intuitive while simultaneously providing the robot withclearer, higher-quality data. Liang et al. [110] proposed using the IntuitiveSurgical da Vinci robotâs controllers as an interface to teleoperate an ABBYuMi robot, allowing users to collect time-synchronized vision and motiondata while demonstrating complex bi-manual tasks. While this approachprovides useful guidelines for designing teaching interfaces, the use of daVinci controllers is not inherently intuitive for novice users [111, 112], high-lighting the need for training methodologies tailored to users without priorrobotics experience.A key challenge in robot learning from human demonstrations is thevariability in human-provided data. Novice users often produce inconsistentor suboptimal demonstrations, limiting learning and generalization [5, 10].While expert demonstrations offer higher-quality data, they are costly anddifficult to obtain. The skill gap between experts and novices further com-plicates the process, as novices struggle to replicate expert techniques, andexperts may find it difficult to articulate their decision-making process. Toaddress this, structured training methodologies leveraging expert data areneeded to help novices consistently provide high-quality demonstrations.4.2 Related Work4.2.1 Human Teaching Behavior in Robot LearningResearch on human teaching behavior in robotics reveals considerable vari-ability in how individuals approach teaching robots [56]. This diversityencompasses both heterogeneityâwhere demonstrators exhibit multiple dis-tinct yet equally optimal behaviorsâand suboptimalityâwhere demonstra-tions are suboptimal relative to an objective reward function [105]. Inves-tigating the impact of this diversity, Mandlekar et al. [52] found that LfDmodels trained on demonstrations from a single proficient user achievedhigher success rates than those trained on larger datasets containing demon-strations from users with varying levels of expertise. Similarly, Gopalanet al. [14] demonstrated that without explicit guidanceâsuch as videos orsubtask breakdownsâonly a third of demonstrators provided compatibledemonstrations. These findings emphasize the critical need for structuredteaching guidance and user training to ensure high-quality and consistentdemonstrations for effective robot learning.Studies have also examined specific teaching strategies. Khan et al. [59]explored human teaching behavior in a one-dimensional classification taskand identified three primary strategies: the extreme strategy, where partic-604.2. Related Workipants begin with extreme examples and gradually refine their demonstra-tions toward the decision boundary; the linear strategy, where participantsfollow a structured sequential order; and the positive-only strategy, whereparticipants provide only positively labeled examples. This variability high-lights the diverse approaches humans take when teaching, even in simpletasks. Similarly, Cakmak and Lopes [113] studied human teaching behaviorsin sequential decision-making tasks and found that novice participants of-ten provide suboptimal demonstrations, though their performance improvessignificantly with guidance or training.Kaochar et al. [114] investigated teaching styles and interaction patternsin a simulated vehicle control task, revealing diverse interface usage amongparticipants. Their findings led to key design guidelines for human-robotinteraction interfaces, including (1) enabling fine-grained learning assess-ments, (2) supporting bootstrapped teaching that allows iterative refine-ment through feedback, and (3) allowing teachers to provide both positiveand negative teaching examples. Inspired by these principles, our approachintegrates feedback mechanisms that allow users to observe the robotâs per-formance after each demonstration. Additionally, we provide users withpractice time, enabling them to explore different teaching styles before fi-nalizing their demonstrations.4.2.2 Teaching Guidance and User TrainingWhile LfD research has extensively explored policy-learning methods, com-paratively less attention has been given to the quality of teaching and strate-gies for improving it [8, 16]. Effective teachingâwhether for humans orrobotsârequires an understanding of the learnerâs existing knowledge, theuse of appropriate instructional strategies, and the ability to adapt basedon feedback. Teaching robots presents additional challenges, as users mustalso account for the robotâs unique learning constraints and requirements.Cakmak and Takayama [103] investigated the effectiveness of instruc-tional materials designed to help users teach robots. They found that mostparticipants preferred working directly with the robot before completingthe instructional materials, often leading to poor demonstrations. Addi-tionally, they reported that video-based instruction was more effective thantraditional written user manuals. More recently, Ajaykumar et al. [102] pro-posed a curriculum-based training program focused on robot manipulationand compared it to self-practice training. Mohseni-Kabir et al. [107] devel-oped an interactive system for hierarchical task teaching, but its relianceon explicit instructions limits its scalability. Our approach, in contrast, fo-614.3. Proposed Approachcuses on enabling users to teach robots effectively without requiring directexpert supervision. Instead, we emphasize guiding users in selecting optimaldemonstrations that maximize learning and generalization.Orendt et al. [115] compared graphical tutorials and verbal instructionsfor one-shot task learning and found that participants were often disengagedwhen the task was simple. This underscores the importance of designingtraining methodologies suited to the complexity of the task. Additionally,motivating users by showing them the direct impact of their demonstra-tions on robot performanceâan approach suggested by Sena and Howard[13]âcan improve engagement. Users who observe poor robot performanceresulting from their demonstrations become more motivated to refine theirteaching methods, ultimately improving learning outcomes [116].In this chapter, we propose training methodologies that leverage pre-recorded expert data to train novice users efficiently while minimizing theneed for direct expert supervision. Our approach incorporates feedback onrobot performance to encourage users to improve their demonstrations. Toevaluate the effectiveness and transferability of the acquired teaching skills,users are assessed not only on the training task but also on a novel task.This ensures that the training enhances both demonstration quality andadaptability, ultimately improving the overall effectiveness of LfD in real-world applications.4.3 Proposed ApproachWe propose training techniques designed to transfer expert-level teachingskills to novice users without requiring direct expert involvement. To assessthe effectiveness of these techniques, we compared them to a baseline dis-covery training method, where users learned through trial and error withoutany guidance, as shown in Figure 4.1. Accordingly, three distinct traininggroups were considered:1. Discovery Group: Participants in this group explored the task spaceindependently. They were given time to practice with the robot, re-flect on their performance, and identify areas for improvement in sub-sequent demonstrations. This group relied solely on trial and error todevelop effective teaching strategies.2. Observational Group: Participants in this group learned by watch-ing an expert demonstration. They viewed a video of an expert per-forming the task, focusing on the expertâs movements and strategies624.4. ExperimentsFigure 4.1: Overview of the proposed training methodologies for improvingdemonstration quality. Two training methodologies leverage expert data,where users undergo training before practising with the robot. These werecompared to discovery training, in which users engage in unguided practicewith the robot.for maneuvering the robot. After the observation session, they prac-tised the task to replicate and apply what they had learned.3. Kinesthetic Group: Participants in this group learned through phys-ical experience using the hand-over-hand training technique. Specif-ically, they passively held the robot while expert trajectories werereplayed, allowing them to feel the robotâs motion and observe itskinematics first-hand. This sensory experience was intended to helpparticipants internalize the task dynamics before practising indepen-dently.4.4 ExperimentsThis experiment aimed to evaluate the effectiveness of different trainingtechniques on the quality of participantsâ demonstrations, as measured bythe robotâs ability to learn and generalize the task. Additionally, it examinedthe transferability of the acquired teaching skills to a new context, assessingwhether participants could effectively apply their learning to a different task.We recruited participants (N = 30) with different levels of expertise inrobotics, as detailed in Section 3.4.2.634.4. Experiments(a) (b)Figure 4.2: (a) Top view of the experimental setup for the writing task, and(b) Side view of the experimental setup showing the gripper holding themarker in a custom compliance holder.4.4.1 Experimental TasksTwo tasks were designed for the user study: one for training and the other forassessing the transferability of acquired teaching skills to a different context.Both tasks utilized the UR5 robot platform described in Section 3.4.2.Training Task: Pick-and-PlaceThe training task is the pick-and-place task previously described in Sec-tion 3.4.2.Transfer Task: Writing TaskThe transfer task required precise end-effector control to guide the robot inwriting letters on a whiteboard. Similar to the pick-and-place task, this taskdid not require prior experience but demanded fine motor control to achievea smooth and accurate trajectory.As shown in Figure 4.2, the UR5 robot was equipped with a Robotiq2F-85 two-finger gripper, which securely held a marker. To ensure safe andcontrolled operation over the writing surface, the marker was placed inside acustom compliance holder. A whiteboard with predefined boundary markerswas positioned beneath the robotâs gripper. Participants were instructedto draw the letter âCâ within the designated area while maintaining fluidmotion, minimizing jerky movements, and staying within the boundary.644.4. ExperimentsFigure 4.3: Overview of the user study procedure: (1) Baseline: Usersprovided baseline demonstrations from all four pickup locations, (2) Feed-back: The robot replayed the demonstration from position 4, (3) Training:Each user was assigned to one of the three training groups, (4) Retentiontest: Users provided four demonstrations, replicating the baseline proce-dure, and (5) Transfer test: Users provided four demonstrations for thewriting task.4.4.2 User Study ProcedureAt the beginning of the study, participants were informed that they wouldteach a robot two tasks using kinesthetic teaching, namely teaching therobot by physically guiding the robot to perform a task [17].Then, the experiment proceeded with four main stages, with the exper-imenter explaining each stage before it began and addressing any clarifyingquestions from participants, as illustrated in Figure 4.3. The four stageswere as follows:1. Baseline Stage: This stage is the pick and place task described inSection 3.4.2.2. Training Stage: For this stage, participants were randomly assignedto one of three training groups: Discovery, Observational, or Kines-thetic. All groups trained at the most challenging pickup location(Location 4 in Figure 3.3e) over three trials of equal duration.654.4. Experimentsâ¢ Discovery Group: Participants explored and refined their teach-ing strategies independently. Participants were instructed to fo-cus on smoothness and efficiency. Participants were trained forthree trials. Each trial lasted three minutes.â¢ Observational Group: Participants watched a video of an ex-pert demonstrating the task 6. After watching the video, theyhad two minutes to practice applying what they observed. Thevideo was replayed at the start of each trial to reinforce learning.Participants were trained for three trials.â¢ Kinesthetic Group: Participants passively held the robot asit replayed a pre-recorded expert trajectory, allowing them tophysically experience the task dynamics. After each replay, theypractised for two minutes. This was repeated for three trials.3. Retention Test: After training, participants performed the pick-and-place task again at all four pickup locations (Figure 3.3e), followingthe same procedure as in the baseline stage.4. Transfer Test: To assess the transferability of skills, participantswere asked to teach the robot to write the letter âCâ within desig-nated boundaries on a whiteboard (Figure 4.2). They provided fourdemonstrations, with the robot reset to its initial position after eachattempt.After completing the baseline and retention tests, participants completedthe NASA Task Load Index (TLX) questionnaire [1] to assess their perceivedtask load during training. Following the training stage, they completed theSUS [117] questionnaire to provide a subjective evaluation of the trainingtechniques.Finally, participants filled out a demographic questionnaire, collectinginformation about their (a) age, (b) gender, (c) hand dominance, and (d)occupation. To control for potential confounding factors, they were alsoasked about their prior experience with robotics, including (a) years of pro-fessional or research experience in robotics, (b) any formal robotics training(university courses, tutorials, or workshops), (c) whether they had physi-cally interacted with a robot before, and (d) if so, which robot and in whatcontext. Lastly, participants were asked whether they believed their perfor-mance had improved after training and in what areas, as well as whetherthe training had helped in the writing task and, if so, in what ways.6https://youtu.be/nPul8WRr9Ok664.5. Analysis4.4.3 ParticipantsWe recruited participants for our user study through word of mouth, ad-vertisements on the university campus, and social media. A total of 30participants (25 male, 5 female) with an average age of 27 years took part inthe study. All but two participants were right-handed. The participants hadvarying levels of expertise in robotics, with 43% reporting no prior formaltraining in robotics and no previous physical interaction with a robot. An in-formed consent was obtained from each participant before the experimentalsessions.4.5 AnalysisThe data collected from the user study was used for learning and general-ization of the task on the robot. The aim is to assess the impact of trainingon the quality of demonstrations, which is reflected in both task learningand task generalization performance.After the training for the pick-and-place task, we followed the sameprocedure described in Section 3.4.3 to evaluate the task and generalizedperformance using the same success criteria. For the writing task, we em-ployed a similar approach as in the pick-and-place task. The starting andending points of the C letter were used to evaluate the learned model foreach participant. Additionally, 16 new locations for the starting and end-ing points of the letter were defined within the task boundary to assess thegeneralization capability of the learned model, as shown in Figure 4.4.A trajectory generated by a learned model is considered successful if itremains within the boundary and follows a smooth path. Smoothness isquantified using jerk and curvature; high values penalize the success score.The overall success score for a trajectory is computed using a weighted sumof boundary compliance, jerk, and curvature, as shown in Equation 4.1.Boundary compliance is defined as the percentage of the trajectory thatremains inside the task boundary. Both jerk and curvature are normalizedusing the minimum and maximum values across participants.S = w1B + w2(1â Jn) + w3(1â Cn) (4.1)where S represents the success of the trajectory, B is boundary compliance(higher is better), Jn is the normalized jerk (lower is better), and Cn is thenormalized curvature (lower is better). The weighting factors w1, w2, and w3determine the relative importance of each term. The overall phase success674.5. AnalysisFigure 4.4: The boundary in the writing task with the 16 generated general-ized points at the start and end locations for evaluating model generalization.for either task performance or generalized performance is then computed asdescribed in Equation 3.2.4.5.1 Objective Measures1. Demonstration Time: The elapsed time measured from the momentthe participant initiated the robotâs movement to demonstrate the taskuntil they completed the demonstration.2. Task Performance Success Rate: The average success of the modellearned from the participantâs data across the four starting locationsin the pick-and-place task and the start and end location of the writingtask.3. Generalized Performance Success Rate: The average success ofthe model learned from the participantâs data across the 16 newlydefined locations in both the pick-and-place task and the writing task.4.5.2 Subjective Measures1. NASA Task Load Index (NASA-TLX):We used the NASA-TLXquestionnaire [1] to measure the subjective task load experienced by684.6. Hypothesesparticipants before and after training. The questionnaire is composedof six items, each requiring participants to rate their perceived taskload across different dimensions on a 21-point scale.2. System Usability Scale (SUS): To evaluate the usability of thetraining techniques, we used the SUS [117]. The SUS comprises tenquestions, each prompting participants to assess various aspects ofsystem usability on a 10-point scale, from which an overall usabilityscore is derived. A key advantage of the SUS is that it is a well-established metric with benchmark comparisons across various userinterfaces and applications, enabling a general assessment of systemusability relative to existing interfaces [118].3. Open-Ended Questions: We included open-ended questions to ex-plore participantsâ perceptions of the trainingâs effectiveness in im-proving their performance in both the retention and transfer tests (asshown in Appendix D.2). If participants found the training beneficial,they were asked to specify in what aspects. These responses providedinsights into participantsâ understanding of demonstration quality andtheir perspectives on what constitutes effective teaching for the robotto learn and generalize after training.4.6 HypothesesWe hypothesize that training users before they teach robots significantlyimproves the quality of demonstrations, thereby enhancing the robotâs per-formance in task learning and generalization. Additionally, we propose thattraining techniques involving exposure to expert data will lead to greaterimprovements compared to those relying solely on trial-and-error practice.Lastly, we expect that the proposed training techniques will help reduceusersâ perceived task load and enhance the usability of the robotic system.Based on these premises, we formulate the following hypotheses for ourexperiment:â¢ H1-a: Training groups with exposure to expert data (i.e., observa-tional and kinesthetic groups) will achieve significantly greater im-provements in learned model task performance success rates comparedto the trial-and-error (i.e., discovery) group.â¢ H1-b: Training groups with exposure to expert data (i.e., observa-tional and kinesthetic groups) will achieve significantly higher learned694.7. Resultsmodel task performance success rates in the transfer test compared tothe trial-and-error (i.e., discovery) group.â¢ H2-a: Training groups with exposure to expert data (i.e., observa-tional and kinesthetic groups) will achieve significantly greater im-provements in learned model generalized performance success ratescompared to the trial-and-error (i.e., discovery) group.â¢ H2-b: Training groups with exposure to expert data (i.e., observa-tional and kinesthetic groups) will achieve significantly higher learnedmodel generalized performance success rates in the transfer test com-pared to the trial-and-error (i.e., discovery) group.â¢ H3-a: User training will significantly reduce participantsâ perceivedtask load.â¢ H3-b: The kinesthetic and observational groups will experience asignificantly greater reduction in perceived task workload comparedto the discovery group.â¢ H3-c: The kinesthetic and observational groups will report signifi-cantly higher training system usability scores compared to the discov-ery group.4.7 Results4.7.1 Objective MeasuresCompletion TimeFigure 4.5 presents the average demonstration time for the training groups(Discovery, Observational, and Kinesthetic) across baseline, retention andtransfer phases. Overall, the demonstration time is high in the baselineacross all groups, especially for the observational training group. The timedecreased significantly in the retention and transfer tests, highlighting theimpact of training on the demonstration time.Examining changes in demonstration time from baseline to retentionreveals notable trends. The observational group exhibited the highest im-provement, reducing their mean demonstration time by 55.8%, from 121.30seconds (95% CI: 79.70 to 162.90) to 53.62 seconds (95% CI: 46.83 to 60.41).The kinesthetic group followed by a reduction in their mean demonstration704.7. ResultsFigure 4.5: Demonstration time across the baseline, retention, and transferphases for the Discovery, Observational, and Kinesthetic training groups.Error bars represent 95% confidence intervals (CI).time by 52.4%, from 85.47 seconds (95% CI: 70.32 to 100.62) to 40.70 sec-onds (95% CI: 35.78 to 45.62). The discovery group also demonstrateda 41.6% improvement, decreasing from 109.52 seconds (95% CI: 88.23 to130.81) to 63.94 seconds (95% CI: 53.90 to 73.98). These trends suggestthat all training conditions contributed to improved task efficiency, with thekinesthetic group achieving the shortest demonstration time in the retentiontest, while the observational group showed the largest percentage reduction.A mixed ANOVA revealed a significant main effect of training on demonstra-tion time (F (1, 26) = 25.86, p < 0.001, η2 = 0.50), confirming that trainingsignificantly reduced demonstration time across all groups. However, no sig-nificant main effect of group was found, but the trend suggests a potentialadvantage for observational and kinesthetic training over discovery training.During the transfer phase, the kinesthetic group maintained the short-est demonstration time, averaging 23.66 seconds (95% CI: 20.77 to 26.55),followed by the discovery group at 26.12 seconds (95% CI: 22.33 to 29.91),while the observational group exhibited the longest demonstration time of35.68 seconds (95% CI: 30.04 to 41.32). A Kruskal-Wallis test revealed a sig-nificant difference between groups (H = 13.31, p = 0.001). Posthoc Dunnâstest with Bonferroni correction indicated that the observational group hadsignificantly longer completion times than both the discovery (p = 0.016)and kinesthetic (p = 0.002) groups.714.7. ResultsFigure 4.6: Task performance success rates for the models learned fromparticipant data across the baseline, retention, and transfer phases for theDiscovery, Observational, and Kinesthetic training groups. Error bars rep-resent 95% confidence intervals (CI).Task Performance Success RateFigure 4.6 presents the learned model success rates for the three traininggroups across the baseline, retention, and transfer phases. Overall, the kines-thetic group demonstrated the highest mean performance across all phases,whereas the discovery group exhibited the lowest success rates. Confidenceintervals (CI) indicate some overlap in baseline performance across groups;however, clearer distinctions emerge in the retention and transfer phases,particularly between the kinesthetic and discovery groups.Examining changes in success rates from baseline to retention revealsnotable trends. The kinesthetic group exhibited the highest improvement,with a 19.5% increase from 0.77 (95% CI: 0.56 to 0.98) to 0.92 (95% CI: 0.84to 1.0), whereas the observational group improved by 16.4% from 0.73 (95%CI: 0.50 to 0.96) to 0.85 (95% CI: 0.75 to 0.95). The discovery group expe-rienced a slight decline of -2.9% in task success from 0.68 (95% CI: 0.46 to0.90) to 0.66 (95% CI: 0.40 to 0.92). These trends suggest that both obser-vational and kinesthetic training contributed to improved retention of skills,with the kinesthetic group achieving the highest absolute performance. No-tably, the retention success rate for the kinesthetic group 0.92 (95% CI: 0.84to 1.0) and observational group 0.85 (95% CI: 0.75 to 0.95) are substan-tially higher than that of the discovery group 0.66 (95% CI: 0.40 to 0.92);partially supporting H1-a. Although statistical analysis did not reveal a724.7. ResultsFigure 4.7: Generalized performance success rates across the baseline, reten-tion, and transfer phases for the Discovery, Observational, and Kinesthetictraining groups. Error bars represent 95% confidence intervals (CI).significant difference between groups, the observed success rate trends indi-cate a potential advantage for kinesthetic and observational over discoverytraining.An additional key finding concerns variability across groups in bothbaseline and retention. The non-parametric Fligner-Killeen test for vari-ance equality at baseline indicated no significant differences between groups(p = 0.891), suggesting that all groups started with comparable variabilityin learned model success before training. However, after training, variancedifferences became significant in the retention phase (p = 0.020). Post-hocpairwise analysis with Bonferroni correction revealed that the kinestheticgroup exhibited significantly lower variability in learned model success ratethan the discovery group (p = 0.035), indicating that kinesthetic trainingled to more consistent task success rates.During the transfer phase, the kinesthetic group maintained the highestsuccess rate 0.94 (95% CI: 0.90 to 0.98), followed by the discovery group0.91 (95% CI: 0.84 to 0.98) and the observational group 0.90 (95% CI: 0.83to 0.97); partially supporting H1-b. The kinesthetic group exhibited thesmallest confidence interval, indicating greater consistency in transfer per-formance. While all groups achieved high success in the transfer phase, thekinesthetic group demonstrated the most stable and reliable outcomes.734.7. ResultsGeneralized Performance Success RateFigure 4.7 presents the generalized success rates for the models learned fromparticipant data for the three training groups across the baseline, retention,and transfer phases. The trends follow a similar pattern to the task perfor-mance success rate, where the kinesthetic group consistently achieved thehighest success rates, while the discovery group exhibited the lowest perfor-mance. Confidence intervals (CI) show substantial overlap in the baselinephase, but in the retention and transfer phases, the kinesthetic group main-tains a clear advantage, particularly in terms of consistency.Examining improvements from baseline to retention, the discovery groupshowed a slight increase of 1.7%, while both the observational and kinestheticgroups improved by 19.0%, and 15%, respectively. These trends suggest thatobservational and kinesthetic training facilitated better skill retention andgeneralization, with the kinesthetic group achieving the highest absoluteperformance of 0.77 (95% CI: 0.75 to 0.79) compared to 0.75 (95% CI: 0.71to 0.79) in the observational group and 0.61 (95% CI: 0.47 to 0.75); sup-porting H2-a. While the statistical analysis did not reveal a significantdifference between groups, the observed generalized success rate trends in-dicate a potential advantage for kinesthetic and observational training overdiscovery.Variability analysis provided further insights into training effects. TheFligner-Killeen test revealed no significant differences in baseline varianceacross groups (p = 0.842), confirming similar levels of variability beforetraining. However, in the retention phase, variance differences became sig-nificant (p = 0.0006). Posthoc pairwise analysis with Bonferroni correctionshowed that observational and kinesthetic groups had significantly lowervariance than the discovery group (p = 0.022 and p = 0.004, respectively).This finding indicates that while overall mean success rates did not signif-icantly differ, kinesthetic and observational training led to more consistentperformance, reducing variability in generalized success rates for retention.During the transfer phase, the kinesthetic group maintained the highestsuccess rate 0.86 (95%CI: 0.80 to 0.92), followed by the discovery group witha success rate of 0.81 (95%CI: 0.75 to 0.87) and observational group 0.78(95%CI: 0.69 to 0.87) groups; partially supporting H2-b. The kinestheticgroup exhibited the smallest confidence interval, highlighting greater con-sistency in transfer test performance. The non-parametric Kruskal-Wallistest found a statistically significant difference in transfer success rates (p =0.034). Posthoc pairwise Conoverâs test with Bonferroni correction revealeda significant difference between the discovery and kinesthetic groups (p =744.7. ResultsFigure 4.8: Boxplots of NASA TLX dimensions comparing Baseline andRetention phases across training groups (Discovery, Observational, Kines-thetic).Table 4.1: NASA TLX dimensions [1] for each training group at Baselineand Retention phases (21-point scale).TLX Dimension Discovery Observational KinestheticBaseline Retention Baseline Retention Baseline RetentionMental Demand 10.70± 3.95 8.20± 3.26 9.70± 4.67 7.90± 3.78 6.89± 3.26 5.11± 2.93Physical Demand 11.90± 3.28 10.90± 5.53 11.30± 3.83 11.60± 3.53 9.00± 4.21 7.22± 3.93Temporal Demand 8.30± 2.63 8.60± 3.69 8.10± 2.47 7.40± 4.84 5.11± 2.67 6.00± 4.42Performance 13.20± 3.36 16.00± 4.16 13.20± 4.02 15.40± 4.60 11.33± 4.58 14.56± 3.94Effort 12.50± 3.89 10.00± 3.89 10.70± 3.40 9.60± 2.72 9.78± 4.24 8.78± 5.61Frustration 6.80± 5.85 5.90± 4.20 6.60± 4.33 6.56± 6.06 5.78± 6.08 5.78± 6.080.042).4.7.2 Subjective MeasuresPerceived Task LoadFigure 4.8 presents the NASA TLX ratings across six workload dimensionsfor the discovery, observational, and kinesthetic training groups. The corre-sponding mean and standard deviation values for each group at the Baselineand Retention phases are provided in Table 4.1. Overall, the kinesthetictraining group demonstrated the highest improvement in physical demand754.7. ResultsFigure 4.9: SUS scores across the three training groups (Discovery, Obser-vational, Kinesthetic).and perceived performance, while the observational group showed the great-est improvement in temporal demand and frustration. Lastly, the discoverygroup exhibited the highest improvement in mental demand and effort.For mental demand, the discovery group showed a decrease from (10.70±3.95)7 at baseline to (8.20 ± 3.26) at retention, suggesting a significant re-duction in cognitive workload. The observational group showed a moderatedecline from (9.70 ± 4.67) to (7.90 ± 3.78), while the kinesthetic group ex-perienced a decrease from (6.89± 3.26) to (5.11± 2.93). A mixed ANOVArevealed a significant main effect of training on mental demand (F (1, 26) =11.24, p = 0.002, η2 = 0.30), supportingH3-a, while no significant differencewas found between groups: i.e., no support for H3-b.For perceived performance, the kinesthetic group showed the most con-siderable increase, from (11.33± 4.58) at baseline to (14.56± 3.94) at reten-tion. The discovery and observational groups also demonstrated increasesin perceived performance, though to a slightly lesser extent. A mixedANOVA revealed a significant main effect of training on perceived perfor-mance (F (1, 26) = 15.34, p < 0.001, η2 = 0.37); supporting H3-a, while nosignificant difference was found between groups; no support for H3-b. Forthe remaining workload dimensions, reductions from baseline to retentionwere observed across all groups; however, these changes were not statisticallysignificant.7mean± std764.7. ResultsTraining System UsabilityFigure 4.9 presents the SUS scores for the three training groups. The re-sults indicate that usability ratings were similar across all groups. A one-way ANOVA revealed no significant differences in SUS scores between thetraining groups (F (2, 18) = 1.99, p = 0.156), suggesting that the perceivedusability of the system was consistent across training conditions; no sup-port for H3-c. According to the global SUS benchmark established bySauro and Lewisâderived from an analysis of 446 studies spanning vari-ous systemsâthe mean SUS score is (68 ± 12.5) [118]. One-sample t-testscomparing the SUS scores of each training group to this benchmark showedno statistically significant differences. This suggests that all training ap-proaches resulted in usability ratings that align with established usabilitystandards, indicating that users found the system similarly usable across alltraining conditions.Open-Ended Questions8Participants provided valuable insights into their perceived performance im-provement after training in both the retention task (pick-and-place) and thetransfer task (writing), and what aspects they felt they had improved in.Overall, 90% of participants reported improvement in retention, reflectingsignificant gains in understanding and executing the task. However, thenumber of participants reporting improvement decreased to 59% for thetransfer task.In the retention task, both the kinesthetic and observational groups hadall participants (100%) reporting improvement in their performance aftertraining. Participants in the kinesthetic group highlighted significant gainsin movement strategy, intuitive control, and precision, as evidenced by thecomment, âI was shown an alternate solution to the joint constraints, whichgave me more ideas of optimal path planning. It may have improved evenmore if the training included a variety of âexpert solutionsâ to give me moreoptions/ideas for my solutions.â Another participant commented, âI foundout I can move the wrist joint!â Participants in the observational groupemphasized better synchronization of robot joints and increased confidence8A formal Thematic Analysis (TA) was not conducted for these open-ended responses.Representative quotes were selected to illustrate recurring observations based on theirclarity and relevance to the research questions rather than through a coding frameworkor frequency counts. Thus, these qualitative findings should be interpreted as illustra-tive rather than exhaustive. Future work could apply a full TAâideally with multiplecodersâto systematically identify codes, themes, and their prevalence.774.8. Discussionin movements. One participant remarked, âI understand better how theUR5âs joints move and whatâs the most efficient path to takeâ. Anotherhighlighted their progress by stating, âBecoming more smooth and confidentin the motionâ. In the discovery group, 70% reported improvements. Oneparticipant noted, âI learned the best path for the pick-and-place task, andwhich joint is the most effective to control to achieve my goal.âIn the transfer task, the kinesthetic group had the highest number ofparticipants reporting improvement, with 67% noting contribution of thetraining to their performance. A participant mentioned, âThe training gaveme a stronger understanding of the degrees of rotation of the robotic armat each joint, which made it easier to intuitively understand how I mighthave to move it to get it to move in a certain wayâ. The discovery groupfollowed with 60% of participants reporting improvement, primarily relatedto familiarity with the robot and better stability during drawing tasks. Oneparticipant remarked, âthe training gave me a hint on how to control thejoint effectively to get the robot moving in the way that I want. Especially, Ilearned how hard I should push or pull for these jointsâ. The observationalgroup followed, with 50% of the participants reporting improvement. Par-ticipants in this group noted enhanced understanding of robot movementsand improved fine control. As one participant stated, âIt helps me under-stand how the robotâs joints move together, as well as the force to apply andthe resistance from the robot to expectâ.4.8 DiscussionIn this chapter, we proposed two training techniques that utilize expertdata without requiring an expert to be physically present in the trainingprocess. We compared these techniquesâkinesthetic training and obser-vational trainingâagainst a trial-and-error (discovery) training method toevaluate their effectiveness. Our results demonstrate that kinesthetic train-ing led to the highest success rates in both the retention and transfer phases,followed closely by observational training, while discovery training resultedin the lowest performance. These findings highlight the substantial impactof guided training approaches on task success, skill retention, and general-ization, even with just a few minutes of training.The kinesthetic group exhibited the highest improvement in task successrate, with a 19.5% increase from baseline to retention, achieving the highestretention success rate of 0.92. Additionally, this group demonstrated su-perior consistency in performance, as indicated by significantly lower vari-784.8. Discussionability compared to the discovery group. In the transfer phase, kinesthetictraining resulted in the highest success rate of 0.97, demonstrating effectiveskill generalization. Moreover, it led to a 52.4% reduction in demonstrationtime, making participants significantly more efficient. The effectiveness ofkinesthetic training aligns with previous research that used the same hand-over-hand technique with practice in surgical training [112]. Similar stud-ies have shown that kinesthetic guidance leads to significantly faster taskcompletion and fewer errors compared to trial-and-error learning in surgi-cal tasks. However, previous studies have not explored its impact on tasklearning and generalization, which our findings extend by demonstrating itseffectiveness beyond a single-task setting.The subjective measures for kinesthetic training were consistent with theobjective results. Participants in this group reported the highest improve-ments in physical demand reduction and perceived performance compared tothe observational and discovery groups. Additionally, all participants in thisgroup attributed their performance improvements directly to the training.Many participants commented that the training helped them develop bettermovement strategies and a deeper understanding of the robotâs kinematics.Interestingly, one of the participants suggested that their performance couldhave improved even further if they had been exposed to multiple expert tra-jectories during training. This insight opens an avenue for future researchâ exploring the impact of multi-expert datasets on training effectiveness.Training with multiple expert trajectories could help broaden participantsâunderstanding of optimal movement strategies, allowing them to create moreeffective demonstrations for the robot to learn from.The observational training group followed the kinesthetic training groupin performance improvement. Observational training showed the highestimprovement in demonstration time, reducing demonstration time by 55.8%from baseline to retention. However, this group exhibited the longest demon-stration times in the transfer test. This may be attributed to the fact that,at baseline, this group had a 41.9% longer demonstration time than thekinesthetic group and 10.8% longer demonstration time than the discoverygroup. As a result, these participants may have required additional practicetime to achieve a comparable level of proficiency. Nevertheless, the obser-vational group improved retention task success by 16.4% and achieved atransfer success rate of 0.89. Notably, this group showed the highest im-provement in the generalized success rate, with a 19% increase, comparedto 15% in kinesthetic training and only 1.7% in discovery training. Sub-jectively, participants in the observational group highlighted the benefits ofexpert demonstrations, which helped them develop effective motion strate-794.9. Chapter Summarygies before engaging in hands-on practice. Participants also reported thatthis training method boosted their confidence in performing the task. More-over, observational training resulted in the greatest reduction in temporaldemand and frustration, suggesting that watching expert demonstrationsbefore practice helped participants manage their workload more effectively.Observational training has previously been shown to outperform text-basedtutorials and user manuals for robot teaching [103], though past studies havenot evaluated its impact on task learning and generalization, a contributionof this work.In contrast, the discovery training group did not show improvement inretention performance, with a slight decline of -2.9% in task success rate anda minimal increase of 1.7% in generalized success rate. However, despitethese limitations, this group performed well in the transfer task, achieving asuccess rate of 0.92. This suggests that self-guided trial-and-error trainingmay enhance adaptability in novel scenarios. Nonetheless, participants inthis group exhibited greater variability in their performance compared to theobservational and kinesthetic groups. The lack of structured guidance mayhave resulted in a slower learning curve, as it required participants to relyon self-exploration rather than expert-driven instruction. While self-directedexploration has been shown to lead to long-term performance gains [56, 102],it typically requires longer training durations and may lead to frustrationamong participants who struggle to discover optimal solutions on their own.Our findings suggest that while discovery training may be beneficial for long-term learning, it is less effective than structured guidance for immediate taskretention and consistency.4.9 Chapter SummaryIn this chapter, we proposed two training techniques that utilize expertdata, video-based observational training and kinesthetic training, to trainusers before teaching robots. Through a user study, we compared theseapproaches to a trial-and-error method (discovery training). Our results in-dicate that kinesthetic training was the most effective strategy for improvingskill retention and transfer, as it provided direct physical guidance, enhanc-ing movement intuition and execution consistency. Observational trainingalso proved to be highly beneficial, leveraging expert demonstrations to im-prove motion planning and execution. In contrast, discovery training, whileuseful for long-term exploration, led to greater performance variability andlower immediate retention improvements.804.9. Chapter SummaryThese findings contribute to the growing body of research on end-usertraining strategies, highlighting the advantages of structured guidance overunguided trial-and-error learning. Although the present study involved 30participants divided equally into three training groups, the sample size (10per group) limits the statistical power to detect smaller effects. No for-mal power analysis was performed, and while care was taken to recruit adiverse sample, the groups were not systematically balanced for age, gen-der, or prior robotics experience. Future work should therefore include apriori power analysis to determine adequate sample sizes and ensure demo-graphic and experience balance across groups. Future work can build onthese insights by increasing the sample size and focusing on novice userswith no prior robotics experience to better understand the training effec-tiveness for absolute beginners. Another promising direction is exploringhow individual learning preferences impact training outcomes, which couldenable personalized training assignments tailored to different user needs.Additionally, designing adaptive training systems that dynamically adjustbased on user performance would ensure that each participant receives anoptimized training duration that maximizes learning efficiency. Finally, in-corporating multi-expert data in training by exposing participants to diverseexpert demonstrations could enhance their ability to synthesize differentmovement strategies, leading to more adaptable and efficient demonstra-tions. By addressing these future directions, we can further refine trainingmethodologies, making them more effective, personalized, and scalable fora wider range of users. These advancements have the potential to enhancehuman-robot interaction and improve learning outcomes in various applica-tions, from industrial automation to medical robotics.81Chapter 5How Can Everyday UsersEfficiently Teach Robots byDemonstration?95.1 IntroductionSo far, we have discussed how to quantify demonstration quality and trainusers to provide high-quality demonstrations. This chapter complementsthe discussion on quality by focusing on the quantity and distribution ofdemonstrations across the task space. It contributes to achieving the thirdobjective of this thesis by introducing a criterion to identify the most infor-mative demonstrations for the robot and guiding users in providing them.This approach addresses the data sparsity issue in LfD systems, particularlywhen data is collected from novice users [5].While LfD builds upon the successes of standard Machine Learning (ML)methods, which have been applied successfully across a diverse array ofapplications [9], teaching robots through human demonstrations introducesunique challenges. These include constraints such as limited human patienceand the generation of low-quality and inefficient data [17]. The size anddiversity of the training data set will determine the speed and accuracy oflearning, including its generalization characteristics.Human teaching strategies are typically adapted for human learners andmay not align perfectly with the needs of machine learners [58]. Neverthe-less, human teachers can adjust to a specific learnerâs requirements [59]. Tofacilitate this adaptation, we introduce the concept of âteaching guidance,âwhich consists of instructions provided to human teachers with the goal ofinfluencing their choice of examples towards those that are most informative9This chapter is published in Maram Sakr, Logan Zhang, Benjamin Li, HaomiaoZhang, H.F. Machiel Van der Loos, Dana Kulic´, and Elizabeth Croft. âHow Can EverydayUsers Teach Robots Efficiently from Demonstrations?â, the ACM Transactions on Human-Robot Interaction, 2025.825.2. Related WorkFigure 5.1: System Overview: Using information entropy for guiding usersto the highest uncertainty areas in LfD.for a particular learner (e.g., a robot).We aim to help novice teachers provide demonstrations efficiently byusing information entropy as the criterion for selecting demonstrations forrobot learning and generalization through the following contributions: i)introducing task-dependent information entropy into the LfD pipeline todefine uncertainty across all task space areas, as shown in Figure 5.1; ii)proposing the integration of Augmented Reality (AR) with the entropy-based guidance system as a training approach to help novice users betteridentify informative demonstrations; iii) evaluating the proposed trainingapproach in a user study; and iv) comparing robot learning efficiency usingour information entropy approach with a state-of-the-art heuristic rule [13].5.2 Related WorkGeneralization of the demonstrated trajectories is one of the most importantchallenges in LfD systems. The provided demonstrations are merely samplesfrom the entire task space and are used to train a learning model to capturethe underlying latent space, rather than simply replicating them. To achievethis generalization, it is crucial to consider both the diversity [119] and thedistribution [120] of the provided demonstrations over the task space. Inother words, demonstrations should be as diverse as possible to maximizecoverage of the task space while minimizing the number of demonstrationsneeded (i.e., achieving efficiency).From a human social learning perspective, two critical observations about835.2. Related Workteaching guidance were highlighted by Chernova and Thomaz [9]: (i) effec-tive teachers maintain a mental model of learner understanding and (ii)learners assist teachers by expressing their internal state through commu-nicative acts, both verbal and non-verbal. While these principles are centralto human teaching, they are often overlooked in LfD systems, leaving sig-nificant opportunities for improvement. For example, in an LfD user study,participants expressed a need for transparency in the learnerâs knowledgeto better understand the robotâs learning progress [121]. Similarly, Huanget al. [122] demonstrated how robots providing informative demonstrationsto humans can enhance the humanâs understanding of robot objectives inautonomous driving tasks.These insights inform the design of our training and guidance frameworkfor lay users. Our proposed system helps users understand the robotâs learn-ing progress and the impact of demonstration quantity and distribution onthe robotâs performance. By doing so, the framework aims to minimize thetime and effort required from users while ensuring they provide a minimalyet effective set of demonstrations to achieve robust learning and general-ization.5.2.1 Active LearningRobots may become more proactive in their learning by requesting addi-tional demonstrations [123, 124] or seeking clarification [46]. This is whatis called âactive learningâ which makes robot learning adaptive by allowingthe robot to request guidance when facing uncertainties or novel situations.Unlike passive LfD, this approach is valuable in cases with limited initialdemonstrations, helping the robot learn correct actions in real-time and re-ducing unsafe or suboptimal decisions [125]. However, this approach placesa burden on the user to be available for further queries, which may be im-practical.Efforts to reduce this burden have included minimizing human involve-ment, as demonstrated in [126â128], who propose optimizing human time,effort and attention as a cost function. At each step, the robot must de-cide whether to query the user or rely on its autonomous controller. How-ever, these systems depend on the availability of an autonomous controllerto replace human input in certain scenarios, which may be impractical orinfeasible for some tasks. Dass et al. [129] proposed automating parts ofthe data collection and asking for human help only in ambiguous scenarios.Others tried to make the queries user-friendly to make it easier for users torespond [130]. Trinh et al. [131] introduced a self-assessment mechanism,845.2. Related Workwhere robots evaluate the sufficiency of the demonstrations they receiverather than relying on human feedback. DAgger, introduced by Ross et al.[120], combats the distributional shift and unseen state generalization is-sues prevalent in behavioral cloning by supplementing demonstrations withexpert-labelled actions in newly encountered states, thus improving robust-ness. Subsequent work, such as that by Zhang and Cho [132], builds onDAgger with methods to reduce the number of expert labels required dur-ing training. Similarly, Chen et al. [133] propose an approach to streamlinereinforcement learning (RL) by enabling the RL agent to actively query fordemonstrations during training. Other methods, such as those by Dai et al.[134] and Tao et al. [135], leverage expert demonstrations and curriculumlearning to enhance demonstration efficiency in RL. All these strategies as-sume access to expert demonstration data or that the demonstrator is anexpert, aiming to reduce the need for expert intervention during training.In contrast, this chapter proposes an approach for training non-expert usersto provide effective demonstrations, allowing robots to learn efficiently fromusers without prior expertise. Our approach operates without any expert inthe loop or reliance on expert demonstrations.Others have proposed incremental learning approaches that enable robotsto learn and refine their skills from human demonstrations without requiringexpert demonstrations or interventions [136, 137]. Mehta et al. [138] intro-duced a framework, StROL, to ensure the stability and robustness of robotslearning online from human input, addressing challenges such as variabil-ity in feedback and task instability. Calinon and Billard [139] introducedan incremental approach in which users teach robots gestures by demon-strating tasks recorded with a motion tracking system. The robot thenperforms the task, and users refine robotâs skills through kinesthetic teach-ing. This approach relies on the teacher to determine the location of thenext demonstration after observing the robotâs performance in various lo-cations. However, this can be particularly challenging for novice teachers,who may struggle to identify the locations for robot evaluation and decidewhere to provide subsequent demonstrations.To address this challenge, Sena and Howard [13] proposed heuristic rulesfor determining demonstration locations in the task space. These rules in-clude: (i) starting with one demonstration from any location in the taskspace, (ii) continuing to provide demonstrations within 4 cm of the firstdemonstration until it is surrounded by successful test points, and (iii)adding further demonstrations within 4 cm of successful test points in areaswith the highest number of failed test points. While these rules are effectivefor their specific experimental setup, they are difficult to generalize to other855.2. Related Worktasks. To overcome these limitations and create a more general approach,we propose using uncertainty measure (information entropy) to guide usersin providing efficient demonstrations. This approach aims to simplify theteaching process for novice users, making it more efficient and adaptableacross different tasks.5.2.2 Machine TeachingA closely related field of research that considers optimal teaching of ma-chine learning systems ismachine teaching, which aims to design the optimaltraining data to drive the learning algorithm to a target model [55]. Twoprominent methods within this field are the âteaching dimension modelâ,which represents the minimum number of instances a teacher must revealto uniquely identify any target concept [140], and the âcurriculum learn-ing principleâ, which presents training examples in a structured sequence,progressing from simple to complex concepts [141]. These approaches openavenues for exploring optimal teaching strategies specifically for robots. Forinstance, Khan et al. [59] compared the teaching dimension model and cur-riculum learning principle in teaching a simple 1D classification problem,while Cakmak and Lopes [113] studied optimal teaching in sequential deci-sion tasks. Building on this foundation, Brown and Niekum [142] reformu-lated inverse reinforcement learning (IRL) as a machine teaching problem,aiming to identify the smallest set of demonstrations required to effectivelyconvey the reward structure of a task.A critical component of machine teaching is selecting the most informa-tive data points to maximize learning efficiency. In robotics, this is analogousto identifying the most impactful demonstrations in the task space. Selectingthese demonstrations is conceptually similar to choosing exploration areasin reinforcement learning (RL) [143] or defining the query that will maxi-mize the information gained about the userâs preference in active preferencelearning [144]. Maximizing information entropy is a popular approach thathas been used in both reinforcement learning and preference learning [68].Typically, information entropy is used to characterize the performance ofan agent through the definition of a reward function, where the next actionis chosen based on the observed entropy maxima. Bet al. [130] utilize theinformation entropy to select the queries that achieve a balance betweeninformation gain and the ability for the user to answer the question confi-dently. Haarnoja et al. [145] proposed a soft actor-critic policy that aimsto maximize expected reward while also maximizing entropy. That is, tosucceed at the task while acting as randomly as possible to increase infor-865.3. Objectivesmation gain. In robot learning from demonstration problems, the efficiencyof user-provided demonstrations is a key factor in determining how quicklyand effectively the task will be learned by the robotic agent.Building on these ideas, we formulate the problem of selecting the mostefficient set of demonstrations for a robot to learn from as a machine teachingproblem. By leveraging information entropy, we aim to minimize teachingcosts and alleviate the teaching burden on users, especially novices. Further-more, we integrate the proposed entropy-based approach into an augmentedreality (AR) guidance system. This system directs users to areas of high un-certainty in the task space, ensuring that additional demonstrations providemaximum benefit for the robotâs learning and generalization.5.3 ObjectivesThe objective of this research is to enhance human teaching skills to provideefficient demonstrations to robots. Therefore, we propose using informationentropy as the criterion for suggesting to the human demonstrator the mostinformative demonstrations from which the robot can learn. The researchquestions we aim to address are as follows:1. Does information entropy improve robot learning efficiency by guidingdemonstration selection?2. Does an entropy-based guidance system enhance usersâ performancein teaching robots through demonstration?3. How much visual information should the guidance system provide toimprove the userâs teaching efficiency?To address the first question, we compared an entropy-based guidancesystem with a heuristic guidance system as proposed in [13] (described inSection 5.2). To tackle the second question, we incorporated the proposedentropy-based guidance system into a training framework for novice usersto assess whether their teaching skills improvedâspecifically in terms of thenumber and distribution of provided demonstrationsâafter training. For thethird question, we conducted a user study comparing two visual guidancesystems: 1) single point suggestion and 2) heatmap representation of thetask spaceâs entropy values. This comparison aims to determine how theamount of visual information provided affects the usersâ teaching efficiency.875.4. Proposed ApproachFigure 5.2: Overview of the user study procedure. (1) Baseline: The userprovides as many demonstrations as they think would be sufficient for therobot to learn the task. (2) Training: The user is guided to the uncertainareas for three trials. (3) Retention test: The user is evaluated on thesame task without guidance. (4) Transfer test: The user teaches the robotto move from a specific starting point to a wider goal zone without guidance.5.4 Proposed ApproachIn the field of LfD, determining the most effective demonstration set for arobot is a critical challenge that directly impacts learning efficiency, gener-alization, and adaptability to new tasks. This problem can be formulated asa machine teaching problem, where the objective is to identify the optimaltraining set that enables a robot to approximate a target policy with thefewest possible demonstrations. To address this, we propose using uncer-tainty measures to guide the selection of the most informative demonstra-tions, enabling efficient and scalable robot learning.5.4.1 Formulating the Machine Teaching ProblemFollowing the machine teaching framework [146], our objective is to find ademonstration set D that allows the learner (the robot) to approximate atarget policy Î¸â as closely as possible. The machine teaching problem canbe framed as a bilevel optimization problem as follows:885.4. Proposed ApproachminD,Î¸Ë|Î¸Ë â Î¸â|2+Î·|D|, (5.1)where |Î¸Ë â Î¸â|2 quantifies the divergence between the learnerâs policy Î¸Ë andthe target policy Î¸â, and η is a weighting parameter that balances the trade-off between the teaching cost |D| (the cardinality of the demonstration set)and learning accuracy. The learnerâs optimization task, defined as the innerproblem, is to minimize the empirical loss:Î¸Ë = argminÎ¸âÎâ(x,y)âDL(x, y, θ) + λ|θ|2, (5.2)where L(x, y, θ) represents the empirical loss between the true outcome yand the model output θ(x), and λ is the regularization parameter penalizingmodel complexity.The outer optimization in equation 5.1 is the teacherâs problem. Theteacher aims to bring the learner model Î¸Ë close to the target model Î¸â whilealso using a small teaching set (|D|). The inner optimization described byequation 5.2 is the learnerâs machine learning problem [146]. The machineteaching problem can be reformulated as follows:minD,Î¸ËTeachingRisk(Î¸Ë) + η TeachingCost(D) (5.3)s.t. Î¸Ë =MachineLearning(D) (5.4)where TeachingRisk(Î¸Ë) is defined to quantify the teacherâs dissatisfactionwith the learnerâs current model. The target model Î¸â may be incorpo-rated into the teaching risk function, but the formulation does not explicitlyrequire a target parameter Î¸â. In this chapter, we adapt the concept ofteaching risk to focus on the learnerâs generalizability across a predefinedtask space. Additionally, the TeachingCost is defined as the cardinalityof the provided demonstrations as in [142]. By simultaneously minimizingboth teaching cost and teaching risk, our approach optimizes the efficiencyof the robotâs learning process.5.4.2 Incorporating Entropy to Optimize DemonstrationsTo optimize the teaching cost, we propose using information entropy [147] asa measure of uncertainty to guide the demonstration selection process. En-tropy enables the identification of regions where the robot is uncertain aboutits ability to achieve success. These high-uncertainty regions are prioritized895.4. Proposed Approachfor additional demonstrations, reducing the total number of demonstrationsrequired while improving generalization. Mathematically, entropy is definedby the Shannon formula as follows:H(Φ) = ânâi=1p(Ïi) log p(Ïi) (5.5)where p(Ïi) is the estimated probability of a specific outcome Ïi from aset of possible outcomes {Ï1, Ï2, . . . , Ïn}. High entropy values (e.g., whenp(Ï) â 0.5) indicate maximum uncertainty, making these regions primecandidates for additional demonstrations.Uncertainty in robot learning arises from multiple sources, including taskmodel dynamics, variability in learning algorithms, and inconsistencies inrobot sensing and control. Each of these sources contributes to the robotâsoverall uncertainty in achieving task success. In the proposed approach, in-formation entropy is used to quantify this aggregated uncertainty and iden-tify high-uncertainty regions where additional demonstrations can improvelearning efficiency. The probability p(Ïi), representing the likelihood of eachoutcome, can be estimated using empirical success rates, model-based meth-ods like regression or Gaussian Processes, or heuristic-based scoring derivedfrom task-specific features.5.4.3 Application to Task-Specific Success CriteriaIn this chapter, we address uncertainty arising from task dynamics andlearning algorithms by defining task-specific success criteria and evaluat-ing performance across multiple rollouts for stochastic learning policies ora single rollout for deterministic policies. This approach is applicable toany task with well-defined success criteria across the workspace. Here, wefocus on a task where a robot learns to navigate a constrained workspace,a scenario common in domestic, industrial, and exploration settings. Suchtasks typically involve reaching a goal location while avoiding obstacles andremaining within a defined workspace. Success for these tasks is defined bythe following criteria:1. The robotâs end effector reaches the target location.2. No collisions occur with obstacles in the environment.3. The robot remains within the defined workspace boundary throughoutthe trajectory.905.4. Proposed ApproachFigure 5.1 illustrates an example task using the proposed entropy-basedapproach. In this example, a user demonstrates to the robot how to retrieveitems from a shelf. The objective is to minimize the number of demonstra-tions while achieving effective generalization across the shelf area. Afteran initial demonstration, the robot learns a model of the task, and theworkspace is discretized into small regions. The robotâs performance is thenevaluated in each region based on the success criteria defined above. Giventhe binary nature of the generated trajectory (success or failure) in eachregion, entropy for each region is calculated as follows:H(r) = âp(r) log p(r)â (1â p(r)) log(1â p(r)) (5.6)where p(r) denotes the estimated probability of success within region r,while 1 â p(r)denotes the estimated probability of failure within region r.This probability of success p(r) for each region is calculated as a weightedsum of task-specific features, averaged over the number of rollouts:p(r) =âNn=1wTxNruns,âiwi = 1, (5.7)where w represents the weights assigned to each task-specific feature x, andNruns denotes the total number of rollouts. The definition of success featuresand desired trajectory characteristics is task-dependent. The weight vectorallows prioritization of certain features based on their relative importanceto the task. For the navigation task in this study, the estimated probabilityof success is computed using the success criteria defined above as follows:p(r) =âNn=1(w1 · (1â dgoal norm) + w2 · nncollision norm + w3 · ninside norm)Nruns,(5.8)where dgoal norm is the normalized distance from the trajectory endpoint tothe goal, nncollision norm is the normalized number of trajectory points thatare not in collision with obstacles, and ninside norm is the normalized numberof trajectory points that are within the defined workspace boundaries. Nrunsis the number of rollouts. Each feature is normalized by its minimum andmaximum observed value across all trajectories to ensure consistent scaling.The weights w1, w2, and w3 are empirically set to w1 = 0.3, w2 = 0.4, andw3 = 0.3, reflecting a comparable level of significance of all task successfeatures for this specific task.915.4. Proposed ApproachTo ensure efficient learning, regions with the highest entropy are prior-itized for additional demonstrations. The next region to receive a demon-stration is selected by identifying the region with the maximum entropyvalue:rnext = argmaxrâRegionsH(r). (5.9)This iterative process continues until the entropy across all regions issufficiently low, signifying the robotâs confidence in consistently performingthe task throughout the workspace. Notably, this entropy-based approachis independent of the userâs expertise level, learning algorithm, or task com-plexity. If initial demonstrations are of low quality, the system can promptadditional demonstrations to ensure effective learning and robust general-ization.5.4.4 Visual Guidance Using Augmented Reality (AR)To guide users to areas of high uncertainty, we utilize a Microsoft HoloLensAR headset [148, 149]. The headset visualizes entropy values within thetask space, giving users intuitive feedback on the robotâs learning progress.Providing the user with more insight into the learning process may leadto more effective teaching. However, providing too much information mayoverwhelm and hinder the user, especially one that is untrained. To findan adequate amount of information to be presented in the visual interfaceabout the robotâs learning progress, we proposed two visualization schemes(Figure 5.2-2) as follows:â¢ Heatmap Visualization: Displays entropy values across the taskspace as a color-coded heatmap.â¢ Single-Point Visualization: Highlights only the most uncertainpoint where additional demonstration is needed.Using AR for guidance makes the system applicable to both 2D and 3Dtasks, enhancing situational awareness by overlaying feedback directly onthe task, unlike the computer screen-based feedback in [13].5.4.5 Task LearningFor task learning, we used the Task Parameterized Gaussian Mixture Model(TP-GMM) [76], as detailed in Appendix A. In our experimental task, we925.5. Evaluationused the same hyperparameters for the TP-GMM model to ensure determin-istic outcomes. This enabled us to evaluate the model with a single rollout,simplifying the computational process. Since the system was evaluated withusers in an online setting, minimizing computational time was essential tomaintain responsiveness and usability.5.5 EvaluationTo evaluate the proposed entropy system, a preliminary experiment in sim-ulation comparing the proposed approach and one of the state-of-the-artheuristic guidance rules is presented. It aims to study the impact of theproposed approach on robot learning efficiency. After that, a user study ispresented to explore the contribution of the proposed approach to improvinghuman teaching skills.5.5.1 Experimental TaskFigure 5.3 represents the experimental task, in which a Kuka IIWA LBR14robot is taught to navigate a 2D maze by drawing a trajectory on the white-board from a starting zone to a target point. Similar success criteria as inSection 5.4 are used. The robot should avoid collisions with the obstaclesin the workspace and it should not go outside the workspace specified by atwo-dimensional bounding rectangle (45 cm by 72 cm) drawn on the white-board. The starting zone is defined by a 45 cm by 15 cm rectangle. Inthe experiment, the starting zone is discretized to a 4 x 14 test grid with56 circles separated by 0.5 cm. This task was designed to resemble a gen-eral robot navigation scenario in a constrained workspace. Its complexitywas intentionally balanced to ensure it was neither too simpleârequiring notraining for users to grasp how to teach it to the robotânor too challengingfor the targeted novice users.Participants use the joystick to teach the robot to navigate through themaze. The teaching process is finished if the robot can generate a âsuccess-fulâ trajectory for 90% of the points in the starting zone to the goal position.The success criteria of the trajectories are similar to what is defined in theexample task in Section 5.4. Microsoft Hololens is used to overlay a geomet-rically accurate starting zone on the real environment, offering user feedbackon the robotâs progress and guiding efficient demonstration, as shown in Fig-ure 5.2.A modified version of this task was used in the transfer test for evaluatingthe userâs performance in new tasks. The training task was flipped; the goal935.5. EvaluationFigure 5.3: Experimental setup used in the user study.point became a starting point and the starting zone became the goal zone.This goal zone was shortened to a 4 x 8 test grid, as shown in Figure 5.2.5.5.2 Performance MetricsQuantitative measures1. Task Completion Time: We measured the total time, t, requiredby the user to complete the experiment task.2. Number of Demonstrations: We measured the number of demon-strations |D| that achieve at least 90% coverage of the starting zone.3. Teaching Efficacy: Efficacy refers to the ability to perform a task toa satisfactory or expected degree. Here, we used the teaching efficacydefined in [13], which is the ratio between the successful points,|IË|, andthe total number of tested points, |I|, (ϵ = |IË||I|).4. Teaching Efficiency: Efficiency in any given application is oftencontext-dependent; however, typically it is desirable to minimize thetotal number of demonstrations required because this is correlatedwith both the time spent teaching and the space needed to store data.Here, we used the teaching efficiency defined in [13], which is the effi-cacy normalized by the number of the provided demonstrations, |D|,η =ϵ|D| . (5.10)945.5. EvaluationFigure 5.4: The average number of demonstrations and robot learning ef-ficiency for both the entropy rule and the heuristic rule with planner andexpert demonstrations.Qualitative measures1. NASA Task Load Index (NASA-TLX): This questionnaire [1] iscomposed of six questions asking the participants to rate their per-ceived task load in six different aspects on a 21-point scale.2. System Usability Scale (SUS). This scale evaluates the usabilityof the proposed training system. The SUS [117] questionnaire includesten questions asking the participant to rate different aspects of the sys-temâs usability on a 10-point scale. An overall score is then calculated.5.5.3 Preliminary Experiment: Guidance Rule inSimulationIn this experiment our goal is to compare the entropy-based guidance rulewith the heuristic guidance rule proposed in [13] (described in Section 5.2).Various factors influence robot learning and generalization, including thequality, quantity, and distribution of provided demonstrations. Here, wespecifically focus on the guidance rule that governs the ânumberâ and âdis-tributionâ of provided demonstrations, while keeping all other factors con-stant. To ensure consistent demonstration quality, we employed the motionplanner RRT-Connect [150] to generate demonstrations for both guidancerules. Trajectories were generated from all points within the starting zoneto the goal point and saved for the experiment. Additionally, to simulatea real-case scenario involving human demonstrations, we collected expertdemonstrations, performed by one of the paperâs authors, for all pointswithin the starting zone of the experimental task shown in Figure 5.3.For the comparison study, for each guidance rule, we developed a Pythonscript to guide the robotâs learning process. The experimental setup was955.5. EvaluationFigure 5.5: Performance metrics for comparing heatmap and single-pointgroups in baseline, retention, and transfer tests.simulated with RViz, the standard tool for visualization and interactionwith robot applications implemented in ROS. The script initially proposesa point in the starting zone, and its corresponding saved trajectory (eitherfrom the motion planner or expert demonstrations) is retrieved. Then, theTP-GMM model is trained by this trajectory and tested with all points inthe starting zone to identify the successful and failed trajectories. Usingthe guidance rule, the script selects the next point in the starting zonefor the next demonstration. These steps are repeated until the learningmodel generates successful trajectories for 90% or more of the points in thestarting zone. The same procedure is repeated 56 times for both guidancerules until every point in the task space has been tested as the initial point.Finally, the two guidance rules are compared with respect to the number ofdemonstrations needed to generate successful trajectories for 90% or moreof the starting zone.HypothesesThe experimental hypotheses are chosen to test whether robot learning effi-ciency, Equation 5.10, improves using the proposed approach compared tothe heuristic rule. The alternative hypothesis is formally defined as:â¢ H1(a): For robot learning using planner demonstrations, learning965.5. Evaluationefficiency using the entropy guidance rule is significantly higher thanlearning efficiency using the heuristic rule.â¢ H1(b): For robot learning using human expert-generated demonstra-tions, learning efficiency using the entropy guidance rule is significantlyhigher than learning efficiency using the heuristic rule.ResultsFigure 5.4 shows the mean and 95% confidence interval of the number ofdemonstrations and robot learning efficiency in 56 trials using the heuristicrule and entropy rule with both expert and planner demonstrations. Overall,using the entropy rule achieves a higher robot learning efficiency than theheuristic rule, for both planner and expert demonstrations. A 2x2 BayesianANOVA was conducted to investigate the impact of a) guidance rule, andb) demonstration type on robot learning efficiency. Both the guidance rule(BF10 = 7.43Ã 1015) and demonstration type (BF10 = 2.82Ã 1013) show alarge effect on robot learning efficiency. In addition, robot learning efficiencyusing either guidance rule strongly depends on the demonstration type (thequality of the provided demonstrations) (BF10 = 184.11). These resultssupport H1(a) and H1(b).Interestingly, a slight decrease in the demonstrationâs quality (i.e., fromthe planner to the expert demonstrations) causes a drastic decrease in therobot learning efficiency by 200% using the heuristic rule, while using theproposed entropy rule the efficiency decreased by only 33.9%. This showsthe importance of the guidance rule in robot learning from demonstrationsand its contribution to the robot learning efficiency which cannot be com-pensated only by improving the demonstration quality. Thus, it is importantto guide the users in providing an efficient set of demonstrations from whichthe robot can learn.5.5.4 User Study ExperimentFrom the simulation experiment results, we found that the entropy guidancerule significantly improves robot learning efficiency. Therefore, we utilizedthis rule in a training framework for users to guide them to provide anefficient set of demonstrations for the robot. Figure 5.2 shows an overviewof the user study procedure as follows:1. Baseline: The user provides as many demonstrations as they thinkwould be sufficient for the robot to generate successful trajectories975.5. Evaluationfrom any point in the starting zone to the goal position. The provideddemonstrations are used to train a TP-GMM model and evaluate theperformance which is provided back to the user via the Hololens asvisualized feedback. The blue circles represent successfully learnt tra-jectories while the red circles represent failed learnt trajectories. Thebaseline phase represents the initial interaction of the users with therobot.2. Training: The user provides an initial demonstration, and then thelearning model is evaluated across the starting zone. The entropyis calculated in each area and visualized through the HoloLens as aheatmap or single-point, according to the training group. The userprovides an additional demonstration in the highest entropy point andthe entropy visualization is updated, and so on. This continues untilthe robot learns successful trajectories for 90% or more of the startingzone. Each user was trained for three trials.3. Retention Test: This step is similar to the baseline to evaluate theperformance after training.4. Transfer Test: The user teaches the robot to move from a spe-cific starting point to a goal zone without guidance, described in Sec-tion 5.5.1.The users were asked to fill in the NASA-TLX questionnaire [1] aftertheir baseline and retention tests to see the effect of the training on their per-ceived task load. They were also asked to fill in the SUS questionnaire [117]after the training to get their subjective evaluation of the proposed train-ing system. Lastly, a semi-structured interview was conducted before andafter training to inquire about usersâ reasoning behind their selection of thenumber of provided demonstrations and their respective placements. Theseinterviews were conducted with the intention of delving into usersâ mentalmodels of robot learning before and after training.HypothesesThe experimental hypotheses are chosen to test whether the userâs teachingskills measured through robot learning efficiency are significantly improvedusing the proposed approach. They are defined as:985.5. Evaluationâ¢ H2: Robot learning efficiency will be significantly improved aftertraining the human teacher using the proposed approach (over bothgroups).â¢ H3: The heatmap visualization group will achieve a significantly higherimprovement in robot learning efficiency than the single-point visual-ization group on the retention test (i.e., same training task).â¢ H4: The heatmap visualization group will achieve a robot learningefficiency that is significantly higher than the single-point visualizationgroup on the transfer test (i.e., on a novel task).The user study is a between-participants design, so each participant isassigned to one group: either the heatmap group or the single-point group.A priori power analysis was conducted using G*Power version 3.1.9.7 [151] todetermine the minimum sample size required to test the study hypotheses.Results indicated the required sample size to achieve 80% power for detect-ing a medium effect size (Cohenâs f = 0.3), and a type I error rate α = 0.05,is N = 24, or 12 participants per condition. Accordingly, the results re-ported below are for a population of 24 participants (14 male, 10 female;ages µ = 24, Ï = 4). We recruited participants for our user study throughadvertisements posted on the University of British Columbia (UBC) cam-pus and social media. Prior to conducting the study, we obtained informedconsent from each participant before commencing the experiment.ResultsQuantitative MeasuresFigure 5.5 depicts the mean and 95% confidence interval for all performancemetrics across baseline, retention, and transfer tests for both the heatmapand single-point groups. Overall, there is a noticeable improvement in allperformance metrics during the retention and transfer tests when comparedto the baseline performance. Efficiency improved by 140% and 164% fromthe baseline to the retention for both heatmap and single-point groups,respectively, and by 146% and 198% from the baseline to the transfer testfor both heatmap and single-point groups, respectively. Interestingly, usersâperformance improved more in the transfer test than in the retention testcompared to their baseline performance. Given that the efficiency metricencapsulates both the number of demonstrations and efficacy, the statisticalanalysis was centred on efficiency.995.5. EvaluationBayesian mixed analysis of variance (ANOVA) showed a strong impact ofthe training on efficiency when comparing the baseline to the retention test(BF10 = 1387.16); supporting H2. The same analysis showed inconclusiveresults on the impact of the visualization type on efficiency (BF10 = 0.4); nosupport for H3 and H4. Furthermore, the Bayesian independent samplest-test showed inconclusive results on the comparison between the transfertest efficiency in both heatmap and single point groups (BF10 = 0.60). Thisresult highlights the effectiveness of the proposed entropy-based guidancesystem, irrespective of how the entropy information is visually presented tothe user.We also investigated the distribution of the demonstrations over the taskspace across the baseline, retention and transfer tests. This shows the differ-ent strategies users adopted to distribute their demonstrations over the taskspace. Figure 5.6 shows a heatmap representing the number and locationsof the provided demonstrations across different stages of the experimentfor both heatmap and single-point groups. The number of the provideddemonstrations notably decreased from the baseline to the two tests. Themedian number of baseline demonstrations was 6 and 7, respectively, for theheatmap and single-point groups, decreasing to a median of 4 in the reten-tion and transfer tests for both groups. Compared to the baseline whichhas a higher number of demonstrations from points in the interior of thestart zone, the retention and transfer test demonstrations are concentratedat the corners. This highlights the change in usersâ strategy for selecting thenumber and locations of demonstrations.The proposed approach implicitly addresses the issue of low-quality demon-strations from novice users. If a user provides low-quality demonstrations,the system will request additional demonstrations to improve task learningand generalization. For instance, Figure 5.7 illustrates the pattern of robotlearning efficacy for each provided demonstration from three different usersalong with the distribution of the demonstrations over the starting zone.The first user provided high-quality demonstrations, so only three demon-strations were sufficient for the learning model to generalize across the entiretask space. The second user provided mixed-quality demonstrations, hencea higher number of demonstrations was required to achieve generalizationacross the task space, with an efficacy of 96%. The third user providedlow-quality demonstrations, thus the learning model only generalized across43% of the task space with six demonstrations. Here, we used task learningperformance as a measure of quality, as proposed in Chapter 2.Qualitative MeasuresPerceived Task Load: Figure 5.8 illustrates the box plots for both the1005.5. Evaluation(a) Heatmap group(b) Single-point groupFigure 5.6: The number and distribution of the demonstrations in the start-ing zone for the baseline, retention, and transfer tests for both the (a)heatmap group and (b) single-point group. Users provided a high num-ber of demonstrations and distributed them across the starting zone in thebaseline. After training, they provided a lower number of demonstrationsfocused on the corners.1015.5. Evaluation(a) High-quality demonstrations exam-ple(b) Mixed-quality demonstrations exam-ple(c) Low-quality demonstrations exampleFigure 5.7: An example of high-, mixed-, and low-quality demonstrations, il-lustrating their relationship to learning efficacy and their distribution acrossthe task space.heatmap and single-point groups following the baseline and retention tests.It shows that both effort and frustration have decreased after training inthe heatmap group while only the effort decreased in the single-point group.In contrast to the quantitative measures, usersâ perceived performance aftertraining was lower than before training in both groups. In general, the taskload metrics statistically indicate no significant difference before and afterthe training. This suggests that the training did not have an adverse impacton usersâ perceived task load or lead to overwhelming experiences.System Usability: The usability score for the heatmap group is (M =75.21; 95% CI, 64.99 to 85.42), while in the single point group (M = 62.71;95% CI, 55.89 to 69.53). According to the global benchmark for SUS createdby Sauro and Lewis through surveying 446 studies spanning different typesof systems, the mean given score is (68± 12.5) [118]. When comparing thisglobal benchmark mean score with the scores obtained from the two traininggroups, the observed differences were inconclusive (Bayesian one-sample t-test (BF10 = 0.75, BF10 = 0.89), respectively). This suggests that both1025.5. Evaluationtraining approaches are usable from the userâs perspective.Interviews10: Participants employed diverse strategies when determin-ing the number and placements of demonstrations during the baseline phase.Among the 24 participants, 19 opted to establish a grid of points, strategi-cally encompassing both the corners and intermediary positions. As artic-ulated by one participant: âWell, overall, I just wanted to get the cornersand then a few different points along the diagonals in between. I just felt likeit kind of covers the majority of the gridâ, which is reflected in Figure 5.6.In contrast, two other participants opted for a completely random selectionof demonstrations, favoring a higher count of demonstrations (18 and 23)to ensure comprehensive coverage of the starting area. Another participantthought that providing a dense set of points would allow the robot to learnthe distance between the points in the starting zone and generalize well toother areas. Another participant provided three demonstrations that formeda triangle. They thought that the two points forming the base of the trianglewould specify the width for the learning algorithm to interpolate in, whilethe third point would specify the height for the algorithm.After training, the participants approached the task differently and effi-ciently thought about the number and distribution of the provided demon-strations. Almost all participants provided an average number of demon-strations in retention equal to the average number of demonstrations theyprovided in the training, regardless of the training condition. As one partici-pant said, âAfter the second training trial, I realized that I donât need a reallylarge number of points as long as I pick the four corners of the rectangularareaâ. In addition, there was an agreement from several participants in theheatmap group that visualizing the generalization area of each point in thetraining was very helpful in deciding the location of the next demonstrationin the retention test. Some participants counted the number of generalizedpoints around one demonstration in the training and tried to distance thedemonstrations in the retention by that number of points.10The interviews were semi-structured and conducted by the lead researcher, guided bya prepared set of prompts about demonstration selection, reasoning, and training influ-ence. Interviews were audio-recorded and summarized, but a formal Thematic Analysis(TA) with multiple coders and code-frequency counts was not performed. Instead, rep-resentative quotations were chosen for clarity and relevance to the research questions.Consequently, the qualitative findings should be interpreted as illustrative rather than ex-haustive, and future work could employ a full TA to systematically identify codes, themes,and their prevalence.1035.6. DiscussionFigure 5.8: Participants response to NASA-TLX [1] questions after baselineand retention test. There is no significant difference before and after thetraining.5.6 DiscussionThis study demonstrated that a few trials of interactive training and guid-ance for lay users significantly improved their teaching skills, which in turnenhanced robot learning and generalization efficiency. Notably, this learn-ing and generalization occurred online, using demonstrations from teacherswith no prior knowledge of robotics or machine learning algorithms. Byusing the proposed training framework, users develop an understanding ofwhat demonstrations the robot requires to learn efficiently, without neces-sarily understanding how the learning process occurs. Thus, the overarchinggoal of this work is realized by decreasing the teaching cost through guid-ing users to provide a small number of demonstrations that achieve widergeneralization.From the preliminary experiment results and statistical analysis, thereis support for H1(a) and H1(b). Robot learning efficiency using the in-formation entropy rule was significantly higher than using the heuristic rulewith both expert demonstrations and motion planner demonstrations. Fur-thermore, using the same task as in [13] but with a larger scale, we found afailure case for the heuristic rule: it happens in 61% of the total experimenttrials. This failure case happens whenever all the surrounding points aredemonstrated and the learning algorithm performs poorly in learning andgeneralization. This failure case is expected to be even worse with noviceswho tend to provide low-quality demonstrations [5] that are negatively af-1045.6. Discussionfecting robot learning and generalization [152].The proposed entropy-based approach has a potential for generalizationto a wide range of tasks where success criteria can be defined across a taskworkspace. In complex scenarios such as object manipulation, multi-agentcoordination, or dynamic obstacle avoidance, entropy can quantify uncer-tainty arising from task-specific factors like grasp stability, coordination reli-ability, or environmental dynamics. By defining measurable success criteriafor each task, entropy identifies high-uncertainty regions where additionaldemonstrations are most informative. For example, in object manipulationtasks, entropy can prioritize demonstrations in areas where stable graspsare uncertain due to variations in object geometry or surface friction. Sim-ilarly, in dynamic obstacle avoidance, entropy can focus on regions withhigh variability in obstacle motion patterns. This adaptability makes theentropy-based approach a powerful tool for optimizing demonstration se-lection in diverse and complex robotic tasks, reducing teaching costs whileenhancing task generalization.Furthermore, to ensure scalability in more complex tasks, an exhaustiveevaluation of all possible starting regions is not necessary. Efficient heuristic-based or sampling techniques can be employed to identify representativehigh-uncertainty regions, significantly reducing computational overhead. Byconcentrating demonstrations in these targeted areas, the proposed approachachieves scalability without compromising its generalization benefits, evenacross larger or more intricate task spaces.The user study design effectively captures usersâ initial performance withthe robot before any training. By providing feedback on their performance,the need for training is emphasized. During the training phase, users receivefeedback on the robotâs generalization performance after each demonstra-tion. This helps them understand how the distribution of their demonstra-tions influences the robotâs learning. Evaluating the robotâs overall perfor-mance across the entire task space helps avoid the biases that can arise fromassessing only a subset of tasks, as noted in [13]. Because each user com-pletes three training trials, they may become more familiar with the robotâslearning process and the way its policy evolves. As a result, it can be dif-ficult to fully separate this learning effect from the impact of the proposedguidance framework without additional experiments or analysis. In futurework, incorporating control groups or exploring different feedback modalitiescould help disentangle these effects and provide a clearer understanding ofthe guidance frameworkâs contribution to improved teaching performance.From the user study results and statistical analysis, there is support forH2 but no support for H3 or H4. Both training groups showed a signif-1055.6. Discussionicant improvement in learning efficiency from the baseline to the retentionand transfer tests. This underscores the importance of training novice usersbefore teaching the robot, consistent with prior work [16, 103]. Additionally,this study shows the benefit of the proposed entropy guidance system re-gardless of the visualization used in training. Notably, both training groupsachieved similar performance in retention and transfer tests. This is becausemost participants had a preconception of creating a grid of points to coverthe starting zone to help the robot learn better, as shown in Figure 5.6. Sub-sequently, post-training, participants recognized the feasibility of achievinghigh efficiency with a reduced number of demonstrations, contrary to theirinitial assumptions. As shown in Figure 5.5, the efficacy before and aftertraining is similar. This finding contrasts with previous work [13], whichfound that most participants tend to underestimate the number of demon-strations required to teach the robot effectively.Regarding the usability of the guidance system, the heatmap group re-ported a higher SUS score than the single-point group, while both traininggroups showed an inconclusive difference with the global mean score. Weposit that this is because participants in the heatmap condition had access toa larger amount of information during training than the single-point group.The heatmap visualization provided feedback on robot learning progress andan efficient way for deciding their next demonstration. Only one participantcommented that they wished to have a highlight of the highest uncertaintypoint in the heatmap. This is because they sometimes found the colors inthe heatmap were very close to each other, making it hard to define thehighest uncertainty point. Participants in the single-point group did nothave access to the robotâs learning progress and therefore had to depend onthe demonstration point suggestions to find a pattern on how to efficientlyteach the robot.A surprising observation emerged concerning usersâ perceived perfor-mance post-training. Despite a significant quantitative improvement in per-formance, 8 out of the 24 participants reported a decline in perceived perfor-mance compared to before training. This discrepancy was investigated byanalyzing their interview data related to their approach to determining thenumber and locations of demonstrations. These participants experiencedsignificant shifts in their strategies after training, which impacted their con-fidence and perception of performance. For instance, one participant pro-vided 16 demonstrations in the baseline. After training, they provided fourdemonstrations in the retention test and remarked in the interview, âI justused four points, although Iâm not sure I covered 90%, I donât know.â Oth-ers changed their strategies from dense point sets to a more distributed1065.7. Chapter Summaryapproach, albeit with skepticism. These findings suggest the potential needfor adaptive training tailored to individual participantsâ requirements, espe-cially for those with strong pre-training strategy convictions, aligning withprevious research [152].Another interesting observation is that one participant without priorknowledge of robotics realized the importance of the demonstrationâs qual-ity in conjunction with the number and location of the provided demonstra-tions for robot learning and generalization [52]. They tried different startingpoints in the training and then realized that providing smooth demonstra-tions without abrupt corners allows the robot to learn and generalize fasterwith a smaller number of demonstrations, as shown in Figure 5.7. How-ever, it is challenging to extrapolate that all participants would naturallyarrive at this realization without proper training, as in [63]. This under-scores the necessity for an instructional framework to guide participants inoffering demonstrations of both high quality and optimal efficiency, echoingthe emphasis of previous research [5].5.7 Chapter SummaryTo advance the goal of efficient robot learning and generalization, this chap-ter proposed using information entropy as a criterion to guide human teach-ers in selecting the most informative next demonstration. This approachreduces the number of demonstrations needed by identifying those that con-tribute most to learning efficiency. It was validated through two experimentsassessing its impact on both learning performance and generalization, as wellas its effect on improving novicesâ teaching skills. The results showed thatthis method significantly outperforms a state-of-the-art heuristic baseline inlearning efficiency. Furthermore, information entropy was integrated intotwo training schemes and evaluated in a user study, revealing significantimprovements in robot learning efficiency following training in both groups.We believe that the results of this chapter open up several directions forfuture research. The significant improvement in efficiency by merely guid-ing users to strategically distribute the demonstrations suggests that guidingusers to provide high-quality demonstrations, as in Chapter 4, along withtheir good distribution, could further boost learning efficiency. It wouldalso be interesting to test the proposed approach in facilities with real userswithout controlling the conditions. For instance, users would have the free-dom to decide how long they need guidance to ensure they provide the mostbeneficial demonstrations to the robot.107Chapter 6Conclusions and FutureWork6.1 ConclusionsThis thesis explores the challenges of robot learning from demonstrations,focusing on enabling novice users to contribute effectively. Recognizing theinherent variability and potential suboptimality in novice demonstrations,we systematically investigated and developed a holistic approach encompass-ing quality assessment, performance analysis, and targeted training method-ologies.Our initial exploration in Chapter 2 laid the groundwork by establish-ing a simple-yet-effective approach for defining demonstration quality basedon task learning and generalization. The user study revealed that demon-stration quality significantly impacts robot learning, with clear distinctionsbetween slow- and fast-adapters in motor skill acquisition. Demonstra-tion data collected over two sessions on different days allowed us to ex-amine improvements in robot teaching skills. The results indicated thatslow-adapters exhibited substantial improvement from the first to the sec-ond session, while fast-adapters maintained a consistently high performancethroughout. These findings align with motor learning literature, reinforc-ing the importance of practice and adaptation in improving teaching effec-tiveness. Furthermore, the study highlights the significance of practice inenhancing demonstration quality. The strong correlation between task per-formance and generalization suggests that improving initial demonstrationquality leads to more effective robot learning outcomes. These findings un-derscore the necessity of personalized training approaches that account fordifferences in user adaptation rates.Chapter 3 built on this foundation by presenting an extensive set ofmetrics to evaluate demonstration quality, emphasizing consistency as a keyfactor in successful robot learning. Through an in-depth analysis of realdemonstration data from users with varying expertise, we demonstrated that1086.1. Conclusionssmoother, more controlled demonstrationsâcharacterized by minimal varia-tion in pathlength and jerk in both Cartesian and joint spacesâlead to bet-ter learning outcomes and enhanced generalization. Our approach enablesreal-time evaluation of demonstration quality before the learning process,ensuring that only high-quality data is used for robot training. Two userstudies involving distinct tasks and robot platforms confirmed the robust-ness of these metrics. The results suggest that task-specific factors influencethe importance of different metrics, but overall the proposed metrics showa high predictability rate for the task success rate and generalization. Byintegrating the proposed metrics into training and evaluation approaches,we provide a systematic way to improve robot learning outcomes.The insights gained from these analytical chapters directly informed thedevelopment of our training methodologies described in Chapter 4. Weexplored training techniques that utilized expert data to enhance novicedemonstration skills. We compared video-based observational training andkinesthetic training to a trial-and-error discovery method. Our findingsdemonstrated that kinesthetic training was the most effective for improvingskill retention and transfer, as it provided direct physical guidance, allowingusers to develop an intuitive understanding of movement execution. Obser-vational training also proved highly beneficial by leveraging expert demon-strations to improve motion planning and execution. In contrast, discoverytraining, while useful for long-term exploration, resulted in greater perfor-mance variability and lower immediate retention improvements. These find-ings contribute to the broader understanding of end-user training strategiesby highlighting the benefits of structured guidance over unguided trial-and-error learning. The study emphasizes the importance of pre-teaching inter-ventions that enable users to develop higher-quality demonstrations moreefficiently. Additionally, the results highlighted the potential for leveragingexpert data to accelerate novice learning.Finally, in Chapter 5, we addressed the efficiency of robot learning byproposing an innovative approach utilizing information entropy to guidedemonstration selection, significantly reducing the teaching burden whilemaximizing learning outcomes. By strategically reducing the number ofrequired demonstrations while maintaining high learning performance, thisapproach significantly improved both the efficiency of robot learning andgeneralization. Our results showed that information entropy-based guid-ance outperformed state-of-the-art heuristic rules in enhancing learning effi-ciency. Furthermore, we integrated entropy-based guidance into two trainingschemes and validated its effectiveness in a user study. The findings suggestthat guiding users to strategically distribute their demonstrations leads to1096.1. Conclusionssignificant improvements in robot learning efficiency. This approach pro-vides a practical method for reducing the burden on human teachers whileensuring that robots learn from the most informative demonstrations avail-able.While consistency and entropy are treated in separate chapters, theyare conceptually linked in how they both capture aspects of variability indemonstrationsâalbeit from different perspectives. Consistency, as intro-duced in Chapter 3, serves as a local measure of demonstration quality,evaluating how stable and reproducible a userâs demonstrations are withina specific region of the task space. In contrast, the entropy-based approachin Chapter 5 captures global uncertainty, identifying areas where the modellacks confident knowledge and additional data is most needed. Together,these two measures provide a richer understanding of the data landscape:entropy helps determine where to collect new demonstrations, while con-sistency helps assess the quality of demonstrations. Importantly, this com-plementarity raises valuable practical insights. For instance, high-entropyregions may coincide with low user confidence, potentially increasing in-consistency. However, by using consistency metrics within those regions,it becomes possible to filter or refine new demonstrations, ensuring onlyhigh-quality data contributes to learning. This interplay between where todemonstrate and how to demonstrate well highlights the synergy betweenthe two frameworks and reflects the overarching goal of this thesis: to guideusers not only toward informative data collection but also toward skillfulteaching.Overall, this thesis contributes to the field of LfD by addressing fun-damental challenges in demonstration quality, training strategies, and effi-cient data collection. By systematically evaluating and improving the waydemonstrations are collected and utilized, we provide a pathway toward moreeffective, user-friendly, and data-efficient robot learning systems. These con-tributions help bridge the gap between human expertise and robot learning,ultimately advancing the capabilities of robots in real-world applications.The insights and methods developed throughout this thesis have rele-vance beyond robotics research. Practically, they can benefit designers anddevelopers of assistive technologies, collaborative robots in manufacturing,and personal service robotsâany domain where users with little technicaltraining are expected to teach robots new tasks. By making robot learn-ing more intuitive and adaptable, this work empowers a broader range ofusersâincluding novices, educators, and caregiversâto meaningfully inter-act with and train intelligent systems.Moreover, this research offers useful tools for adjacent disciplines such as1106.2. Future Workhuman factors, learning sciences, and cognitive science, where understand-ing human motor behavior and effective teaching strategies is essential. Theconsistency metrics, training interventions, and entropy-guided data collec-tion strategies presented here can inform the design of interfaces, curricula,and feedback mechanisms that support better skill acquisition and transfer.In sum, the contributions of this thesis are not only technical but also con-ceptual, offering building blocks for more inclusive, accessible, and effectivehuman-robot learning ecosystems.6.2 Future WorkThe findings of this thesis open several promising directions for future re-search as follows:Future work could integrate the proposed approach for demonstrationquality (Chapter 2) with the metrics for evaluating consistency (Chapter3) and the training techniques (Chapter 4). This integration could leadto the development of adaptive training systems that dynamically adjustbased on user performance and adaptation. Additionally, providing real-time feedback on usersâ performance could encourage them to improve, en-suring optimal learning outcomes and more personalized training. Exploringthe use of augmented reality (AR) or virtual reality (VR) to provide real-time feedback during training [153â155] could further enhance this process.These technologies could also facilitate communication between humans androbots, enabling robots to convey their internal state or pose queries to usersthrough intuitive interfaces. This aligns with the broader goal of creatingfluid and efficient human-robot interactions.Another promising direction involves incorporating multi-expert data intraining, enabling users to observe and synthesize diverse optimal movementstrategies. This would offer flexibility in teaching robots, allowing users todevelop strategies that are both intuitive and efficient. Furthermore, investi-gating individual learning preferences and incorporating them into trainingmethodologies could make the process more engaging and personalized. Thisdirection aims to replace traditional technical user manuals with adaptivetraining programs tailored to different tasks and scenarios, making the train-ing experience enjoyable and user-centric. Serious games [156, 157], whichhave been shown to improve user engagement and learning outcomes, couldalso be explored as a tool to enhance training experiences.The use of information entropy for optimizing demonstration selectioncould be extended by integrating reinforcement learning [158, 159] or Bayesian1116.2. Future Workoptimization [160, 161] to further enhance efficiency. Additionally, investi-gating how users interact with guidance systems when given greater auton-omy in determining the duration and nature of their training could improveadaptability while maintaining effectiveness. Striking a balance betweenstructured guidance and user-driven exploration could yield more effectiveand scalable learning solutions.Beyond the kinesthetic and teleoperation interfaces studied in this thesis,future work should explore alternative teaching modalities and evaluate theirapplicability to a wider range of robotic platforms. Assessing the long-termretention and transfer of teaching skills across different tasks and environ-ments would provide valuable insights into sustainable training practices.Furthermore, deploying these approaches in real-world settings, such as in-dustrial automation or assistive robotics, would help validate their scalabilityand practicality, particularly for users with no prior robotics experience.Another critical direction is shifting from a one-way demonstration model(where users provide demonstrations and the robot passively learns) to abidirectional interaction model, where the robot plays an active role in thelearning process. In this approach, the robot would assess user skill levelsand performance, deciding whether to fully trust the demonstrations pro-vided or request clarification in cases of uncertainty. Similarly, the robotshould provide real-time feedback on its state and learning progress to en-sure mutual understanding between the human teacher and the roboticlearner. This direction would require ensuring that the user provides high-quality inputs, whether they be demonstrations, feedback, or corrective ac-tions [162, 163].Expanding on the vision of making human-robot interaction more inclu-sive, future research should focus on designing adaptive teaching methodsthat accommodate users of different skill levels, ages, and abilities. Incorpo-rating principles from psychology and pedagogy into training methodologieswould further enhance the effectiveness of user-robot teaching interactions.Additionally, exploring multimodal interaction methods, such as speech, ges-tures, and facial expressions, could create natural, context-aware communi-cation between humans and robots that mirror human-human interactionsin daily life.In this thesis, the PR2, UR5, and KUKA robots provided an opportu-nity to examine demonstration quality across different embodimentsâfroma mobile manipulator to industrial armsâhighlighting how robot morphol-ogy and interaction capabilities shape user teaching strategies. Buildingon this, future work could extend these studies to robots with richer so-cial abilities (e.g., expressive gestures, facial cues, or speech). Investigating1126.2. 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If the demonstrations vary in length, they can be alignedusing an alignment algorithm such as dynamic time warping.Here, we use state measurements defined as:ξn =(tn,xTn , ϵTn) â R8, (A.1)where each data point consists of the time tn, the end-effector position xTn ,and the end-effector orientation represented by quaternions ϵTn .For task learning, we employ a Task-Parameterized Gaussian MixtureModel (TP-GMM) combined with Gaussian Mixture Regression (GMR).TP-GMMs have been widely used in the LfD literature [18, 77], offering ro-bust generalization from a limited set of demonstrations. TP-GMM modelsa task using task parameters defined by a sequence of coordinate frames.In a D-dimensional space, each task parameter or coordinate frame isrepresented by a transformation matrix A â RDÃD that encodes orientationand a translation vector b â RD that defines its origin relative to the globalframe. A K-component Gaussian Mixture Model (GMM) is fitted to thedata in each local frame of reference. Each GMM is defined by:(Ïk, µ(j)k ,Σ(j)k), (A.2)where Ïk denotes the prior probability, µ(j)k represents the mean, and Σ(j)kis the covariance matrix for component k in frame j. The Expectation-Maximization (EM) algorithm is used to estimate these parameters whileensuring that data points from different frames originate from the sameunderlying source.133A.2. Joint Space LearningTo generate a trajectory, the local models must be transformed backinto the global frame and integrated into a single global model. This isachieved through a linear transformation of the local models using theirrespective task parameters, followed by a product of Gaussians. A newGMM with components {Ïk, µk,t,Σk,t}Kk=1 at time t in the global frame {O}is computed as:N (µk,t,Σk,t) â Jâj=1N(A(j)t µ(j)k + b(j)t ,A(j)t Σ(j)k (A(j)t )T). (A.3)GMR is then used to infer the next trajectory point. This process isrepeated at each time step to generate a continuous trajectory. Furtherdetails on this approach can be found in Calinon [76].A.2 Joint Space LearningGiven a task-space trajectory obtained from the TP-GMM model, repre-sented by task position x(t) and task velocity xË(t), the objective is to com-pute a feasible joint-space trajectory, expressed as joint positions q(t) andjoint velocities qË(t), that accurately reproduces the given task trajectory.The differential kinematics equation establishes a linear mapping be-tween joint-space and task-space velocities, which can be used to determinejoint velocities. However, since the Jacobian matrix J of a 7-DOF ma-nipulator is non-square, the direct inverse solution is computed using theMoore-Penrose pseudoinverse Jâ , given by:Jâ = JT (JJT )â1. (A.4)To mitigate issues associated with kinematic singularities, we employ thesingularity-robust (SR) inverse [164]. Since a 7-DOF redundant manipulatorhas excess degrees of freedom relative to its task space, a null space exists,allowing for secondary objectives to be optimized. The null space componentis incorporated following the formulation in [165]:qË = Jâ(q)xË+ (I â Jâ (q)J(q))Ï(q), (A.5)where Jâ is the SR inverse Jacobian, Jâ is the pseudoinverse of J , (IâJâ J)is the null-space projection operator, and Ï(q) is the null-space policy. Thenull-space policy allows for independent joint-space control without affectingthe primary task-space trajectory. To ensure that the generated motion134A.2. Joint Space Learningclosely mimics human demonstrations, we set Ï(q) as the closest humandemonstration in joint space.The first term in (A.5) represents the particular solution to the inversekinematics problem, ensuring that xË is achieved, while the second termprovides a homogeneous solution that does not affect the primary task.A.2.1 Closed-Loop Inverse KinematicsNumerically integrating the open-loop joint velocity solution often leads todrift and accumulated task-space errors. To address these issues, we adoptthe Closed-Loop Inverse Kinematics (CLIK) algorithm, which introduceserror feedback for robustness. The CLIK formulation is given by:qË = Jâ(q)(xËd + kp(xd â x)) + (I â Jâ (q)J(q))Ï(q), (A.6)where kp is a symmetric positive-definite gain matrix, xËd is the desired task-space velocity, and (xd â x) is the position error in task space. The choiceof kp ensures uniform error convergence.A.2.2 Kinematic Singularities AvoidanceThe pseudoinverse method exhibits quadratic convergence when initializednear a solution. However, it suffers from instability near singular points,leading to large joint velocity magnitudes. To address this, we employ thesingularity-robust (SR) inverse [164], also referred to as the damped pseu-doinverse [166]. The SR inverse minimizes the combined norm of the solutionand the error, and is computed as:Jâ = JT (JJT + kI)â1, (A.7)where Jâ is the SR inverse of J , I is the identity matrix, and k is a dampingparameter that balances error minimization and numerical stability. A smallk reduces the error but may result in large values near singularities, while alarge k improves stability at the cost of higher residual errors [167].A.2.3 Null Space OptimizationFor a 7-DOF redundant manipulator, the null space contains motions thatdo not affect the primary task execution. These motions belong to theorthogonal complement of the feasible task-space velocities. The null-spaceprojection ensures that additional joint-space objectives can be incorporatedwithout disturbing the task trajectory.135A.2. Joint Space LearningThe null-space optimization follows the formulation from [165]:qË = Jâ(q)xË+ (I â Jâ (q)J(q))Ï(q), (A.8)where (IâJâ J) projects joint velocities onto the null space, and Ï(q) allowsfor secondary objectives such as joint limit avoidance or energy efficiency.In our work, we set Ï(q) to the closest human demonstration to improvenaturalness and mimicry.A.2.4 Joint-Limit AvoidanceTo prevent joints from exceeding their allowable ranges, joint limits aredefined as lower and upper bounds qL and qU , respectively. After comput-ing joint values using inverse kinematics, each joint is checked to ensureit remains within limits. If any joint approaches its boundary, the inversekinematics computation is adjusted iteratively until a feasible solution isfound.qL â¤ q(t) â¤ qU . (A.9)This iterative process ensures that the generated joint-space trajectoryremains within physically valid joint limits while accurately executing thetask trajectory.136Appendix BLegibility CalculationThe formula for calculating the legibility score is shown in Table 3.1, whereP (Gi|ξ1:t) is the posterior probability of goal Gi given the trajectory segmentξ1:t. The probability is computed using the cost function for each potentialgoal, which calculates the likelihood of a trajectory for a given goal. Itcombines early differentiation and progress towards the goal as follows:C(ξ,G) = w1(1Dearly)+ w2(1Pgoal)(B.1)where Dearly is the early differentiation score, Pgoal is the progress to-wards the goal, and w1 and w2 are the weights for each term. Early differen-tiation measures how distinguishable the trajectory is from other potentialgoals early in the motion as follows:Dearly =1nnâi=1â¥Î¾1:t âGâ¥ (B.2)where n is the number of potential goals, ξ1:t is the early trajectorysegment, and G is the goal. Progress towards the goal measures how directlythe trajectory moves towards the goal as follows:Pgoal =âni=1max(proj(Î¾âi, Gâ), 0)â¥Gâ Î¾0â¥ (B.3)where Î¾âi is the vector segment of the trajectory, Gâ is the vector towardsthe goal, and ξ0 is the starting point of the trajectory.137Appendix CConsistency MetricsC.1 Summary of Metric Ranges for ConsistencyClustersTo support future research and practice, Table C.1 reports the average rangevalues for each of the proposed consistency metrics, computed separatelyfor consistent and inconsistent demonstration clusters in both user stud-ies. While these values are task-specific and depend on factors such asrobot kinematics, task constraints, and user proficiency, they offer practicalreference points that may guide consistency evaluation in similar settings.Notably, the absolute range values differ between the button-pressing andpick-and-place tasks due to differences in task complexity and motion scale.Therefore, these values should be interpreted within the context of eachtask, rather than used for direct cross-task comparison.C.2 Consistency Impact Across DifferentLearning MethodsTo evaluate the robustness of our hypotheses H1-a and H1-b across dif-ferent learning approaches, we trained a diffusion-based trajectory gener-ation model on the pick-and-place task. Denoising Diffusion ProbabilisticModels (DDPMs)[168] have gained popularity in robot policy and trajec-tory generation due to their strong generative performance and ease ofadaptation[169, 170]. Unlike TP-GMM, which models trajectories as se-quences over time, diffusion models treat the entire trajectory as a singlesample. During training, Gaussian noise is incrementally added in a forwardprocess, and a neural network, typically a U-Net [171], is trained to reversethis process by denoising. The model is supervised using a mean squared er-ror (MSE) loss, which predicts the added noise from the ground truth trajec-tory. In our experiment, the model was conditioned on both the pickup andplace poses, including end-effector positions, orientations (quaternions), andgripper states. These conditioning signals were concatenated and provided138C.2. Consistency Impact Across Different Learning MethodsTable C.1: Average range values for each metric in consistent vs. inconsis-tent demonstrations for both experiments.Task Metric Consistent InconsistentButton-Pressing (PR2)Pathlength in Cartesian Space 0.43 2.52Pathlength in Joint Space 1.33 10.71Path Orientation Length 1.13 9.58Jerk in Cartesian Space 1.50Ã103 2.43Ã103Jerk in Joint Space 4.40Ã103 1.39Ã104Manipulability 0.032 0.054Distance to Joint Limits 0.042 0.051Curvature 235.12 236.74Joint Effort 68.93 234.78Legibility 0.88 0.90Pick-and-Place (UR5)Pathlength in Cartesian Space 2.45 10.32Pathlength in Joint Space 7.31 30.67Path Orientation Length 6.05 29.08Jerk in Cartesian Space 1.13Ã 105 4.33Ã 105Jerk in Joint Space 1.38Ã 105 5.68Ã 105Manipulability 0.028 0.044Distance to Joint Limits 0.079 0.205Curvature 2.79Ã 1024 5.58Ã 1024Joint Effort 4.78 5.88at each denoising step to guide the model toward task-relevant trajectories.As shown in Figure C.1a, task performance remained significantly higherin the consistent group compared to the inconsistent group, (F (1, 58) = 8.07,p = 0.006), with average success rates of (0.84 ± 0.02) and (0.70 ± 0.04),respectively. In the generalization performance (Figure C.1b), the consistentgroup also outperformed the inconsistent group, though the difference wasmarginally significant, (F (1, 58) = 3.50, p = 0.066), with success rates of(0.26± 0.01) and (0.22± 0.03), respectively.While both TP-GMM and the diffusion model benefited from consistentdemonstrations, their absolute performance profiles diverged. The diffusionmodel slightly outperformed TP-GMM on the task performance for the con-sistent group (0.84±0.02 vs. 0.79±0.03), reflecting its strength in faithfullyreproducing training data. However, it underperformed TP-GMM in gen-139C.2. Consistency Impact Across Different Learning Methods(a) Task performance success rate (b) Generalized performance success rateFigure C.1: Average task and generalized performance success rates in thepick-and-place task using the diffusion model.eralization: with consistent demonstrations, the diffusion model achievedonly a (0.26 ± 0.01) success rate on novel pickup and place combinations,compared to (0.81± 0.03) for TP-GMM.We attribute this difference largely to data efficiency between the twomodels. In our experiment, each diffusion policy was trained on only fourdemonstration trajectories per participant, an extremely low data regime inwhich the model likely overfitted to the training scenarios. Most diffusion-based robot learning studies utilize an order of magnitude more demonstra-tions to attain robust performance. For example, Diffusion Policy [169] wasbenchmarked with around 100â200 demonstrations per task, and even moredata-efficient variants like the 3D Diffusion Policy (DP3) [170] demonstratedgood performance with some tasks using 10 demonstrations in simulationand around 40 demonstrations per task on real robots. Additionally, DP3incorporates a richer set of inputsâincluding RGB-D images, language in-structions, and 3D voxel grids, whereas our model relies solely on kinematicfeatures. These comparisons highlight the diffusion modelâs sensitivity todata scarcity and suggest that overfitting may have limited its generalizationcapability in our setting. In contrast, TP-GMM explicitly models temporalstructure and can generalize from fewer examples by leveraging time-alignedGaussian components.Despite these differences, the consistency effect emerged in both ap-proaches. In the diffusion model, as in TP-GMM, consistent demonstrationsyielded superior performance metrics relative to inconsistent ones. Thisreinforces our central conclusion: enhancing the quality and consistencyof demonstrations is essential for achieving robust and generalizable robotlearning, regardless of the learning paradigm used.140Appendix DQuestionnairesD.1 Previous Robotics ExperienceThis is the questionnaire that was used in Section 2.4.31411.2.Mark only one oval.FemaleMalePrefer not to say3.4.5.Robot Learning from Demonstration* Indicates required questionPlease record your participant number *Gender *Age *Current Occupation *Major *10/5/25, 1:32 PM Robot Learning from Demonstrationhttps://docs.google.com/forms/d/1DA6CSJL_3CWk0uVffMTr3kXZ2yJkfxnu7iqGQM37Y0M/edit 1/41426.Mark only one oval.LeftRightPART II - Robotics Background7.Mark only one oval.Less than 6 months6 to 12 months1 to 3 years4 years or moreNone8.Hand dominance *How many years of professional or research experience do you have in the area ofrobotics?*Do you have formal training in robotics (university courses, tutorials, workshops, ..etc)? Please answer with "yes" or "No". If your answer is "Yes" please specify thelist of courses*10/5/25, 1:32 PM Robot Learning from Demonstrationhttps://docs.google.com/forms/d/1DA6CSJL_3CWk0uVffMTr3kXZ2yJkfxnu7iqGQM37Y0M/edit 2/41439.Mark only one oval.Yes Skip to question 10No Skip to question 11Physical Interaction context10.Performance11.This content is neither created nor endorsed by Google.Have you physically interacted with a robot? *If your answer is "Yes" please specify the robot and the context in which youphysically interacted with the robot.*Do you think that your performance improved through trials? Please answer with"yes" or "No". If your answer is "Yes" please specify the main items you haveimproved in.*Â Forms10/5/25, 1:32 PM Robot Learning from Demonstrationhttps://docs.google.com/forms/d/1DA6CSJL_3CWk0uVffMTr3kXZ2yJkfxnu7iqGQM37Y0M/edit 3/4144D.2. Open-ended QuestionsD.2 Open-ended QuestionsThis is the questionnaire that was used in Section 4.5.21451.2.3.Mark only one oval.FemaleMaleNon-binary / gender diversePrefer not to say4.Mark only one oval.LeftRight5.Experiment Survey 4General Questions* Indicates required questionParticipant ID *Age *Gender *Hand Dominance *Current Occupation *10/5/25, 1:05 PM Experiment Survey 4https://docs.google.com/forms/d/1jyl5BzT73FulhMxByBT3KmLaqD_JZRKlwXiGKdtvTIM/edit 1/5146Robotics Background6.Mark only one oval.Less than 6 months6 to 12 months1 to 3 years4 years or moreNone7.8.Mark only one oval.Yes Skip to question 9No Skip to question 10Physical Interaction contextHow many years of professional or research experience do you have in the area ofrobotics?*Do you have formal training in robotics (university courses, tutorials, workshops, ..etc)? Please answer with "yes" or "No". If your answer is "Yes" please specify thelist of courses*Have you physically interacted with a robot? *10/5/25, 1:05 PM Experiment Survey 4https://docs.google.com/forms/d/1jyl5BzT73FulhMxByBT3KmLaqD_JZRKlwXiGKdtvTIM/edit 2/51479.Performance10.11.This content is neither created nor endorsed by Google.If your answer is "Yes" please specify the robot and the context in which youphysically interacted with the robot.*Do you think that your performance improved after training? Please answer with"Yes" or "No". If your answer is "Yes" please specify the main items you haveimproved in.*Do you think that the training help you perform better in the writing task?Â Pleaseanswer with "Yes" or "No". If your answer is "Yes" please specify the main itemsyou have improved in.*10/5/25, 1:05 PM Experiment Survey 4https://docs.google.com/forms/d/1jyl5BzT73FulhMxByBT3KmLaqD_JZRKlwXiGKdtvTIM/edit 3/5148Appendix ERegression AnalysisE.1 Regression analysis of task success rateInitially, linear regression was employed, but it captured only a small varia-tion of the success rate using linear relationships with the predictors. Con-sequently, stepwise regression was applied to consider interactions amongthe predictors and their quadratic terms. Additionally, we standardized thepredictors and outcome values to facilitate the interpretation of the maineffects and interaction terms.Table E.1 shows the resulting regression model that represents the rela-tionship between the range of the metrics and the task success rate. Themodel is significant and captures 70.2% of the variance in the success rate.The model shows a significant linear relationship with eight predictors thatare the range of: pathlength in joint space(x2), path orientation length(x3), jerk in Cartesian space (x4), jerk in joint space (x5), distance to jointlimit (x7), curvature(x8), legibility(x10), and the consistency (x11). Whilethe other metrics exhibit a more complex relationship with the success ratethrough interaction and quadratic terms, indicating the importance of eval-uating the metrics together, rather than focusing on any single metric.The visualization in Table E.1 highlights the coefficients and standarderrors of the significant terms. Notably, range of the pathlength in jointspace (x2), orientation pathlength (x3), and their interaction with jerk injoint space (x5) and effort (x9) are crucial for determining success, giventheir higher coefficient values. The negative coefficient of (x2) suggests thatshorter, more consistent paths reduce unnecessary movements, thus loweringthe likelihood of errors [18]. Variability in both pathlength and jerk in jointspace reflects inefficient, erratic movements, detrimental to precision-basedtasks, which is why their interaction term negatively impacts success rate.Conversely, the positive interaction between pathlength in joint space andeffort suggests that aligning effort with path variability improves control andadaptation, enhancing performance.The range of orientation pathlength (x3) has a positive effect on the suc-cess rate, indicating the importance of flexible orientation adjustments to149E.2. Regression analysis of generalized success ratecomplete the task, especially in high-constraint environments. Additionally,its interaction with jerk in joint space positively influences success, suggest-ing that quick, even jerky, orientation adjustments are beneficial in suchcontexts. However, its interaction with effort shows a negative coefficient,indicating that excessive variability in both orientation pathlength and ef-fort reduces success. This suggests that high, consistent effort associatedwith orientation adjustments may impede performance.Similarly, a regression model was developed for the second experiment,examining the relationship between task performance success rate and therange of metrics. Table E.2 illustrates the model and provides a visualiza-tion of the coefficients and standard errors of significant terms. The modelis significant, capturing 88.6% of the variance in success rate. Key interac-tions involve pathlength in Cartesian space (x1), pathlength in joint space(x2), orientation pathlength (x3), jerk in Cartesian space (x4), and jerk injoint space (x5), with higher coefficient values highlighting their importancein estimating success rate. The positive interaction between pathlength injoint space and orientation pathlength suggests that flexible orientation ad-justments, alongside variability in joint movements, enhance performancein dynamic tasks. However, the negative interaction between pathlengthand jerk in Cartesian space indicates that excessive variability in both met-rics can hinder task performance. Moreover, interactions involving jerk injoint space with both orientation pathlength and jerk in Cartesian spaceexhibit contrasting effects: the former negatively impacts success, reflect-ing that erratic joint movements combined with orientation changes reduceperformance, while the latter positively impacts success, suggesting thatcoordinated variability in jerk across joint and Cartesian spaces improvesadaptability, leading to better task outcomes.E.2 Regression analysis of generalized successrateSimilar regression analysis was conducted to explore the relationship be-tween the range of the metrics and the consistency label with the generalizedsuccess rate for the two experiments data. Table E.3 presents the regressionmodel detailing the relationship between the range of the proposed metricsand the generalized success rate of the button pressing experiment. Themodel is significant, explaining 75.6% of the variance in the generalized suc-cess rate. Notably, the same metrics that were important in predicting thetask success rate also contribute the most to the generalized success rate.150E.2. Regression analysis of generalized success rateThese key metrics include the range of pathlength in joint space (x2), therange of path orientation length (x3), and their interaction with jerk in jointspace (x5). This underscores the critical role of these metrics in predictingboth task and generalized success.In the pick and place experiment, the regression model (Table E.4) ex-plains 90.7% of the variance in the generalized success rate. Key interactionsinvolving pathlength in Cartesian space (x1), pathlength in joint space (x2),jerk in Cartesian space (x4), and jerk in joint space (x5) are among the mostimportant terms, similar to the task performance model. This underscoresthe critical role of these metrics in predicting both task and generalizedsuccess. Consistency (x10) also plays a critical role, positively affecting thegeneralized success rate, indicating that consistent demonstrations signifi-cantly improve the robotâs ability to generalize.151E.2. Regression analysis of generalized success rateTable E.1: Regression Model detailing the relation between the range ofthe proposed metrics and the task success rate in the first experiment. x1represents the range of pathlength in Cartesian space, x2 represents therange of pathlength in joint space, x3 represents the range of path orientationlength, x4 represents the range of jerk in Cartesian space, x5 represents therange of jerk in joint space, x6 represents the range of manipulability, x7represents the range of the distance to joint limits, x8 represents the rangeof the demonstration curvature, x9 represents the range of joint effort, x10represents the range of the legibility and x11 represents the consistency.Coefficient Estimate SE t-Statistic Visualization of co-efficients and theirstandard errors(Intercept) -0.5499 0.1092 -5.0347x2 -3.8873 0.7807 -4.9794x3 2.9233 0.6930 4.2184x4 -0.2674 0.0884 -3.0254x5 -0.3258 0.1448 -2.2505x7 -0.1508 0.0612 -2.4649x8 -0.5086 0.1198 -4.2468x10 0.2706 0.0595 4.5450x11 0.7208 0.1574 4.5795x3 : x4 -0.4071 0.1274 -3.1964x1 : x5 0.5346 0.2232 2.3950x2 : x5 -5.1903 1.1900 -4.3617x3 : x5 4.6017 1.0848 4.2420x4 : x5 -0.4149 0.1721 -2.4112x4 : x6 0.3448 0.0848 4.0686x5 : x6 -0.6147 0.1307 -4.7043x2 : x7 -0.2345 0.0641 -3.6568x1 : x8 -0.9810 0.3257 -3.0124x2 : x9 3.6332 0.7405 4.9061x3 : x9 -3.1695 0.7050 -4.4955x6 : x9 0.2374 0.0628 3.7817x8 : x9 -0.3518 0.1390 -2.5321x4 : x10 0.2371 0.0509 4.6618x6 : x10 -0.2263 0.0754 -3.0016x24 0.2007 0.0502 3.9971x25 0.5752 0.1976 2.9117Number of observations: 144Error degrees of freedom: 118Root Mean Squared Error: 0.601R-squared: 0.702Adjusted R-Squared: 0.639F-statistic vs. constant model: 11.1p-value: 9.14e-21152E.2. Regression analysis of generalized success rateTable E.2: Regression Model detailing the relation between the range ofthe proposed metrics and the task success rate in the second experiment.x1 represents the range of pathlength in Cartesian space, x2 represents therange of pathlength in joint space, x3 represents the range of path orientationlength, x4 represents the range of jerk in Cartesian space, x5 represents therange of jerk in joint space, x6 represents the range of manipulability, x7represents the range of the distance to joint limits, x8 represents the rangeof the demonstration curvature, x9 represents the range of joint effort, andx10 represents the consistency.Coefficient Estimate SE t-Statistic Visualization ofcoefficients andtheir standarderrorsIntercept -0.011078 0.090473 -0.12244x1 -1.1226 0.30021 -3.7392x2 0.85762 0.35571 2.411x5 -0.94164 0.2167 -4.3454x2 : x3 3.135 0.57773 5.4264x1 : x4 -2.9919 0.52756 -5.6711x3 : x5 -3.1553 0.68183 -4.6277x4 : x5 2.4976 0.61275 4.0761x1 : x6 -1.3935 0.28438 -4.9002x2 : x6 1.6134 0.29548 5.4603x4 : x8 -1.2869 0.29361 -4.3828x6 : x8 -1.0353 0.21373 -4.8443x1 : x9 1.0596 0.27262 3.8867x2 : x9 -0.99994 0.27296 -3.6633x8 : x9 0.92439 0.15412 5.998x4 : x10 -0.48341 0.18134 -2.6657Number of observations: 60Error degrees of freedom: 44Root Mean Squared Error: 0.392R-squared: 0.886Adjusted R-Squared: 0.847F-statistic vs. constant model: 22.7p-value: 6.95e-16153E.2. Regression analysis of generalized success rateTable E.3: Regression Model detailing the relationship between the range ofthe proposed metrics and the generalized success rate in the first experiment.x1 represents the range of pathlength in Cartesian space, x2 represents therange of pathlength in joint space, x3 represents the range of path orientationlength, x4 represents the range of jerk in Cartesian space, x5 represents therange of jerk in joint space, x6 represents the range of manipulability, x7represents the range of the distance to joint limits, x8 represents the rangeof the demonstration curvature, x9 represents the range of joint effort, x10represents the range of legibility and x11 represents the consistency.Coefficient Estimate SE t-Statistic Visualization(Intercept) 0.27975 0.12896 2.1692x2 -3.5869 0.7299 -4.9143x3 2.8648 0.68134 4.2047x5 -0.49044 0.12662 -3.8734x9 -0.33265 0.11054 -3.0092x11 -1.2349 0.32368 -3.8152x1 : x4 0.82844 0.19886 4.1659x3 : x4 -0.90945 0.25697 -3.5391x2 : x5 -3.7961 0.73839 -5.141x3 : x5 4.7715 0.83905 5.6868x4 : x5 -0.35898 0.13353 -2.6883x4 : x6 0.1458 0.073164 1.9927x5 : x6 -0.86245 0.12318 -7.0017x1 : x7 1.3359 0.28081 4.7574x2 : x7 -0.63637 0.27687 -2.2984x5 : x7 0.27406 0.12249 2.2373x6 : x7 -0.29517 0.061987 -4.7617x2 : x8 1.6195 0.34553 4.687x2 : x9 1.4209 0.27381 5.1892x5 : x9 -0.66221 0.22121 -2.9936x6 : x9 0.44656 0.08497 5.2555x8 : x9 -0.74627 0.18647 -4.0022x2 : x10 -0.40238 0.20063 -2.0056x7 : x10 -0.36737 0.059606 -6.1633x2 : x11 1.1933 0.25787 4.6274x7 : x11 0.93652 0.16325 5.7367x9 : x11 0.46362 0.10807 4.2899x10 : x11 -0.44841 0.17632 -2.5432Number of observations: 144Error degrees of freedom: 116Root Mean Squared Error: 0.549R-squared: 0.756Adjusted R-Squared: 0.699F-statistic vs. constant model: 13.3p-value: 2.43e-24154E.2. Regression analysis of generalized success rateTable E.4: Regression Model detailing the relation between the range of theproposed metrics and the generalized success rate in the second experiment.x1 represents the range of pathlength in Cartesian space, x2 represents therange of pathlength in joint space, x3 represents the range of path orientationlength, x4 represents the range of jerk in Cartesian space, x5 represents therange of jerk in joint space, x6 represents the range of manipulability, x7represents the range of the distance to joint limits, x8 represents the rangeof the demonstration curvature, x9 represents the range of joint effort, andx10 represents the consistency.Coefficient Estimate SE t-Statistic Visualization ofcoefficients andtheir standarderrorsIntercept -2.1426 0.25463 -8.4146x2 1.5602 0.2692 5.7956x5 -1.1614 0.2206 -5.2647x6 0.46128 0.090664 5.0878x9 0.45899 0.085092 5.394x10 6.933 0.83754 8.2778x1 : x2 11.712 1.1528 10.159x1 : x4 -12.053 1.1215 -10.748x2 : x5 -8.8461 0.89576 -9.8755x4 : x5 8.4328 0.85875 9.8198x1 : x6 -1.649 0.27123 -6.0799x2 : x6 2.1474 0.27391 7.8396x7 : x8 -0.22681 0.051082 -4.4401x2 : x9 -4.2964 0.44981 -9.5516x4 : x9 4.946 0.49788 9.9341x7 : x9 -0.28161 0.10056 -2.8003x4 : x10 -4.5763 0.56098 -8.1576x5 : x10 1.6174 0.3304 4.8954x6 : x10 -0.69759 0.2299 -3.0343x9 : x10 -0.74814 0.13176 -5.6778Number of observations: 60Error degrees of freedom: 40Root Mean Squared Error: 0.371R-squared: 0.907Adjusted R-Squared: 0.862F-statistic vs. constant model: 20.5p-value: 6.37e-15155
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Croft, Elizabeth
Kulic, Dana
Liao-McPherson, Dominic
Democratizing robot learning: teaching by demonstration with everyday users in mind
Text