TY - GEN
T1 - Emergence of Motor Synergy in Multi-directional Reaching with Deep Reinforcement Learning
AU - Han, Jihui
AU - Chai, Jiazheng
AU - Hayashibe, Mitsuhiro
N1 - Funding Information:
This work was supported by the JSPS Grant-in-Aid for Scientific Research on Innovative Areas (20H05458) Hyper-Adaptability project.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/11
Y1 - 2021/1/11
N2 - In this study, we apply Deep Reinforcement Learning for handling full-dimensional 7 degrees of freedom arm reaching, and demonstrate the relations among motion error, energy, and synergy emergence during the learning process, to reveal the mechanism of employing motor synergy. Although synergy information has never been encoded into the reward function, the synergy effect naturally emerges, leading to a similar situation as human motion learning. To the best of our knowledge, this is a pioneer study verifying a concurrent relation between the error-energy index and synergy development in DRL for multi-directional reaching tasks. In addition, our proposed feedback-augmented DRL controller shows better capability over DRL only in terms of error-energy index.
AB - In this study, we apply Deep Reinforcement Learning for handling full-dimensional 7 degrees of freedom arm reaching, and demonstrate the relations among motion error, energy, and synergy emergence during the learning process, to reveal the mechanism of employing motor synergy. Although synergy information has never been encoded into the reward function, the synergy effect naturally emerges, leading to a similar situation as human motion learning. To the best of our knowledge, this is a pioneer study verifying a concurrent relation between the error-energy index and synergy development in DRL for multi-directional reaching tasks. In addition, our proposed feedback-augmented DRL controller shows better capability over DRL only in terms of error-energy index.
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U2 - 10.1109/IEEECONF49454.2021.9382755
DO - 10.1109/IEEECONF49454.2021.9382755
M3 - Conference contribution
AN - SCOPUS:85103740890
T3 - 2021 IEEE/SICE International Symposium on System Integration, SII 2021
SP - 78
EP - 82
BT - 2021 IEEE/SICE International Symposium on System Integration, SII 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE/SICE International Symposium on System Integration, SII 2021
Y2 - 11 January 2021 through 14 January 2021
ER -