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Digital Library of the
European Council for Modelling and Simulation |
Title: |
Integrating Simulation With Robotic Learning From Demonstration |
Authors: |
Anat Hershkovitz Cohen, Sigal
Berman |
Published in: |
(2014).ECMS 2014 Proceedings edited
by: Flaminio Squazzoni,
Fabio Baronio, Claudia Archetti,
Marco Castellani European Council for
Modeling and Simulation. doi:10.7148/2014 ISBN:
978-0-9564944-8-1 28th
European Conference on Modelling and Simulation, Brescia,
Italy, May 27th – 30th,
2014 |
Citation
format: |
Anat Hershkovitz Cohen, Sigal Berman
(2014). Integrating Simulation With Robotic
Learning From Demonstration, ECMS 2014 Proceedings edited by: Flaminio Squazzoni, Fabio Baronio, Claudia Archetti,
Marco Castellani European Council for Modeling and Simulation. doi:10.7148/2014-0421 |
DOI: |
http://dx.doi.org/10.7148/2014-0421 |
Abstract: |
Robots that co-habitat an
environment with humans, e.g., in a domestic or an agricultural environment,
must be capable of learning task related information from people who are not
skilled in robotics. Learning from demonstration (LfD)
offers a natural way for such communication. Learning motion primitives based
on the demonstrated trajectories facilitate robustness to dynamic changes in
the environment and task. Yet since the robot and human operator typically
differ, a phase of autonomous learning is needed for optimizing the robotic
motion. Autonomous learning using the physical hardware is costly and time
consuming. Thus finding ways to minimize this learning time is of importance.
In the current paper we investigate the contribution of integrating an
intermediate stage of learning using simulation, after LfD
and before learning using robotic hardware. We use dynamic motion primitives
for motion planning, and optimize their learned parameters using the PI2 algorithm which is based on reinforcement learning. We
implemented the method for reach-tograsp motion for
harvesting an artificial apple. Our results show learning using simulation
drastically improves the robotic paths and that for reach-to-grasp motion
such a stage may eliminate the need for learning using physical hardware.
Future research will test the method for motion that requires interaction
with the environment. |
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