Imitation-based learning of bipedal walking using locally weighted learning
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Loken, Kevin
issued: 2010-01-16T18:34:55Z
issued: 2006
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Walking is an extremely challenging problem due to its dynamically unstable nature. It is further complicated by the high dimensional continuous state and action spaces. We use locally weighted projection regression (LWPR) as a locally structurally adaptive nonlinear function approximator as the basis for learned control policies. Empirical evidence suggests that control policies for high dimensional problems exist on low dimensional manifolds. The LWPR algorithm models this manifold in a computationally efficient manner as it only models those states which are visited using a local dimensionality reduction technique based on partial least squares regression. We show that local models are capable of learning control policies for physics-based simulations of planar bipedal walking. Locally structured control policies are learned from observation of a variety of different inputs including observation of human control and existing parametrized control policies. We extend the pose control graph to the concept of policy control graph and show that this representation allows for the learning of transition points between different control policies.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/18314
note: Imitation-based Learning of Bipedal Walking Using Locally Weighted Learning by Kevin Loken B.A.Sc., Simon Fraser University, 1992 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Science in The Faculty of Graduate Studies (Computer Science) The University Of British Columbia August 2006 © Kevin Loken 2006 11 Abstract Walking is an extremely challenging problem due to its dynamically unstable nature. It is further complicated by the high dimensional continuous state and action spaces. We use locally weighted projection regression (LWPR) as a locally structurally adaptive nonlinear function approximator as the basis for learned control policies. Empirical evidence suggests that control policies for high dimensional problems exist on low dimensional manifolds. The LWPR algorithm models this manifold in a computationally efficient manner as it only models those states which are visited using a local dimensionality reduction technique based on partial least squares regression. We show that local models are capable of learning control policies for physics-based simulations of planar bipedal walking. Locally structured control policies are learned from observation of a variety of different inputs including observa-tion of human control and existing parametrized control policies. We extend the pose control graph to the concept of policy control graph and show that this representation allows for the learning of transition points between different control policies. Kevin Loken University of British Columbia August 2006 iii Contents Abstract ii Contents iii List of Tables v List of Figures vi List of Algorithms vii Acknowledgements viii 1 Introduction 1 1.1 Imitating skillful human motion 1 1.2 Why is motor control hard? 2 1.3 Recent Progress 4 1.4 Goals 6 1.5 Overview of Approach 7 1.6 Contributions 10 1.7 Thesis Organization 10 2 Definitions and Related Work 12 2.1 Definitions 12 2.2 Imitation-based Learning 18 2.3 A Review of Walking …
has type: Thesis/Dissertation
date issued: 2006-11
is shown at: 10.14288/1.0051510
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/18314