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A novel method for learning policies from variable constraint data

A novel method for learning policies from variable constraint data Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations come from movements under different constraints. As a key ingredient, we introduce a small but highly effective modification to the standard risk functional, allowing us to make a meaningful comparison between the estimated policy and constrained observations. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 27 degrees of freedom, and present results for learning from human demonstration. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

A novel method for learning policies from variable constraint data

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References (55)

Publisher
Springer Journals
Copyright
Copyright © 2009 by Springer Science+Business Media, LLC
Subject
Computer Science; Simulation and Modeling; Mechanical Engineering; Computer Imaging, Vision, Pattern Recognition and Graphics; Electrical Engineering; Control , Robotics, Mechatronics; Artificial Intelligence (incl. Robotics)
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1007/s10514-009-9129-8
Publisher site
See Article on Publisher Site

Abstract

Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations come from movements under different constraints. As a key ingredient, we introduce a small but highly effective modification to the standard risk functional, allowing us to make a meaningful comparison between the estimated policy and constrained observations. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 27 degrees of freedom, and present results for learning from human demonstration.

Journal

Autonomous Robots – Springer Journals

Published: Jul 30, 2009

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