Extracting bimanual synergies with reinforcement learning

Kevin Sebastian Luck, Heni Ben Amor

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Motor synergies are an important concept in human motor control. Through the co-activation of multiple muscles, complex motion involving many degrees-of-freedom can be generated. However, leveraging this concept in robotics typically entails using human data that may be incompatible for the kinematics of the robot. In this paper, our goal is to enable a robot to identify synergies for low-dimensional control using trial-and-error only. We discuss how synergies can be learned through latent space policy search and introduce an extension of the algorithm for the re-use of previously learned synergies for exploration. The application of the algorithm on a bimanual manipulation task for the Baxter robot shows that performance can be increased by reusing learned synergies intra-task when learning to lift objects. But the reuse of synergies between two tasks with different objects did not lead to a significant improvement.
Original languageEnglish
Title of host publicationIROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4805-4812
ISBN (Electronic)9781538626825
DOIs
Publication statusPublished - 13 Dec 2017
Externally publishedYes
Event2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 - Vancouver, Canada
Duration: 24 Sept 201728 Sept 2017

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
Country/TerritoryCanada
CityVancouver
Period24/09/1728/09/17

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