Huang, Y and Caldwell, DG (2020) A Linearly Constrained Nonparametric Framework for Imitation Learning. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). International Conference on Robotics and Automation ICRA 2020, 31 May - 31 Aug 2020, Paris, France. IEEE , pp. 4400-4406. ISBN 978-1-7281-7396-2
Abstract
In recent years, a myriad of advanced results have been reported in the community of imitation learning, ranging from parametric to non-parametric, probabilistic to non-probabilistic and Bayesian to frequentist approaches. Meanwhile, ample applications (e.g., grasping tasks and humanrobot collaborations) further show the applicability of imitation learning in a wide range of domains. While numerous literature is dedicated to the learning of human skills in unconstrained environments, the problem of learning constrained motor skills, however, has not received equal attention. In fact, constrained skills exist widely in robotic systems. For instance, when a robot is demanded to write letters on a board, its end-effector trajectory must comply with the plane constraint from the board. In this paper, we propose linearly constrained kernelized movement primitives (LC-KMP) to tackle the problem of imitation learning with linear constraints. Specifically, we propose to exploit the probabilistic properties of multiple demonstrations, and subsequently incorporate them into a linearly constrained optimization problem, which finally leads to a non-parametric solution. In addition, a connection between our framework and the classical model predictive control is provided. Several examples including simulated writing and locomotion tasks are presented to show the effectiveness of our framework.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Feb 2020 15:26 |
Last Modified: | 08 Dec 2020 21:59 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICRA40945.2020.9196821 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157826 |