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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pastor, P. Kalakrishnan, M. Chitta, S. Theodorou, E. Schaal, S. |
| Copyright Year | 2011 |
| Description | Author affiliation: Willow Garage Inc., Menlo Park, CA 94025, USA (Chitta, S.) || CLMC Laboratory, University of Southern California, Los Angeles, USA (Pastor, P.; Kalakrishnan, M.; Theodorou, E.; Schaal, S.) |
| Abstract | Learning complex motor skills for real world tasks is a hard problem in robotic manipulation that often requires painstaking manual tuning and design by a human expert. In this work, we present a Reinforcement Learning based approach to acquiring new motor skills from demonstration. Our approach allows the robot to learn fine manipulation skills and significantly improve its success rate and skill level starting from a possibly coarse demonstration. Our approach aims to incorporate task domain knowledge, where appropriate, by working in a space consistent with the constraints of a specific task. In addition, we also present an approach to using sensor feedback to learn a predictive model of the task outcome. This allows our system to learn the proprioceptive sensor feedback needed to monitor subsequent executions of the task online and abort execution in the event of predicted failure. We illustrate our approach using two example tasks executed with the PR2 dual-arm robot: a straight and accurate pool stroke and a box flipping task using two chopsticks as tools. |
| Starting Page | 3828 |
| Ending Page | 3834 |
| File Size | 2081552 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781612843865 |
| ISSN | 10504729 |
| e-ISBN | 9781612843858 |
| DOI | 10.1109/ICRA.2011.5980200 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-05-09 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Robot sensing systems Grippers Trajectory Cost function Humans Learning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software |
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