Extracting bimanual synergies with reinforcement learning

Kevin Sebastian Luck, Heni Ben Amor

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

4 Sitaatiot (Scopus)

Abstrakti

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.

AlkuperäiskieliEnglanti
OtsikkoIROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
KustantajaIEEE
Sivut4805-4812
Sivumäärä8
ISBN (elektroninen)9781538626825
DOI - pysyväislinkit
TilaJulkaistu - 13 jouluk. 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE/RSJ International Conference on Intelligent Robots and Systems - Vancouver, Kanada
Kesto: 24 syysk. 201728 syysk. 2017

Julkaisusarja

NimiIEEE International Conference on Intelligent Robots and Systems
Vuosikerta2017-September
ISSN (painettu)2153-0858
ISSN (elektroninen)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
LyhennettäIROS
Maa/AlueKanada
KaupunkiVancouver
Ajanjakso24/09/201728/09/2017

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