Democratizing robot learning: teaching by demonstration with everyday users in mind
department or school: Applied Science, Faculty of
department or school: Mechanical Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Sakr, Maram
issued: 2025-10-20T16:14:17Z
issued: 2025
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: 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…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/92666
note: 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 promisin…
has type: Thesis/Dissertation
date issued: 2025-11
is shown at: 10.14288/1.0450488
language: eng
degree discipline: Mechanical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Croft, Elizabeth
contributor: Kulic, Dana
contributor: Liao-McPherson, Dominic
type: Text
identifier URI: http://hdl.handle.net/2429/92666