TY - GEN
T1 - Reinforcement Learning Assist-as-needed Control for Robot Assisted Gait Training
AU - Zhang, Yufeng
AU - Li, Shuai
AU - Nolan, Karen J.
AU - Zanotto, Damiano
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - The primary goal of an assist-as-needed (AAN) controller is to maximize subjects' active participation during motor training tasks while allowing moderate tracking errors to encourage human learning of a target movement. Impedance control is typically employed by AAN controllers to create a compliant force-field around the desired motion trajectory. To accommodate different individuals with varying motor abilities, most of the existing AAN controllers require extensive manual tuning of the control parameters, resulting in a tedious and time-consuming process. In this paper, we propose a reinforcement learning AAN controller that can autonomously reshape the force-field in real-time based on subjects' training performances. The use of action-dependent heuristic dynamic programming enables a model-free implementation of the proposed controller. To experimentally validate the controller, a group of healthy individuals participated in a gait training session wherein they were asked to learn a modified gait pattern with the help of a powered ankle-foot orthosis. Results indicated the potential of the proposed control strategy for robot-assisted gait training.
AB - The primary goal of an assist-as-needed (AAN) controller is to maximize subjects' active participation during motor training tasks while allowing moderate tracking errors to encourage human learning of a target movement. Impedance control is typically employed by AAN controllers to create a compliant force-field around the desired motion trajectory. To accommodate different individuals with varying motor abilities, most of the existing AAN controllers require extensive manual tuning of the control parameters, resulting in a tedious and time-consuming process. In this paper, we propose a reinforcement learning AAN controller that can autonomously reshape the force-field in real-time based on subjects' training performances. The use of action-dependent heuristic dynamic programming enables a model-free implementation of the proposed controller. To experimentally validate the controller, a group of healthy individuals participated in a gait training session wherein they were asked to learn a modified gait pattern with the help of a powered ankle-foot orthosis. Results indicated the potential of the proposed control strategy for robot-assisted gait training.
KW - Assist-as-needed controller
KW - rehabilitation robotics
KW - reinforcement learning
KW - robot-assisted gait training
KW - wearable robotics
UR - http://www.scopus.com/inward/record.url?scp=85095608620&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85095608620&partnerID=8YFLogxK
U2 - 10.1109/BioRob49111.2020.9224392
DO - 10.1109/BioRob49111.2020.9224392
M3 - Conference contribution
AN - SCOPUS:85095608620
T3 - Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
SP - 785
EP - 790
BT - 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
T2 - 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
Y2 - 29 November 2020 through 1 December 2020
ER -