| Record Number | 10446 |
| Reference Type | Conference Paper |
| Author(s) | Pastor, P.;Kalakrishnan, M.;Chitta, S.;Theodorou, E.;Schaal, S. |
| Year | 2011 |
| Title | Skill learning and task outcome prediction for manipulation |
| Journal/Conference/Book Title | Robotics and Automation (ICRA), 2011 IEEE International Conference on |
| Keywords | movement primitives, reinforcement learning, sensory data mining, motor skills |
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.
|
| Notes | clmc |
| URL(s) | http://www-clmc.usc.edu/publications/P/pastor-ICRA2011.pdf
|
| Place Published | Shanghai, China, May 9-13 |
| Short Title | Skill learning and task outcome prediction for manipulation |
