TY - GEN
T1 - Adaptive Assist-as-needed Control Based on Actor-Critic Reinforcement Learning
AU - Zhang, Yufeng
AU - Li, Shuai
AU - Nolan, Karen J.
AU - Zanotto, Damiano
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - In robot-assisted rehabilitation, assist-as-needed (AAN) controllers have been proposed to promote subjects' active participation, which is thought to lead to better training outcomes. Most of these AAN controllers require a patient-specific manual tuning of the parameters defining the underlying force-field, which typically results in a tedious and time-consuming process. In this paper, we propose a reinforcement-learning-based impedance controller that actively reshapes the stiffness of the force-field to the subject's performance, while providing assistance only when needed. This adaptability is made possible by correlating the subject's most recent performance to the ultimate control objective in real-time. In addition, the proposed controller is built upon action dependent heuristic dynamic programming using the actor-critic structure, and therefore does not require prior knowledge of the system model. The controller is experimentally validated with healthy subjects through a simulated ankle mobilization training session using a powered ankle-foot orthosis.
AB - In robot-assisted rehabilitation, assist-as-needed (AAN) controllers have been proposed to promote subjects' active participation, which is thought to lead to better training outcomes. Most of these AAN controllers require a patient-specific manual tuning of the parameters defining the underlying force-field, which typically results in a tedious and time-consuming process. In this paper, we propose a reinforcement-learning-based impedance controller that actively reshapes the stiffness of the force-field to the subject's performance, while providing assistance only when needed. This adaptability is made possible by correlating the subject's most recent performance to the ultimate control objective in real-time. In addition, the proposed controller is built upon action dependent heuristic dynamic programming using the actor-critic structure, and therefore does not require prior knowledge of the system model. The controller is experimentally validated with healthy subjects through a simulated ankle mobilization training session using a powered ankle-foot orthosis.
KW - Assist-as-needed controller
KW - rehabilitation robotics
KW - reinforcement learning
KW - robot-assisted training
KW - wearable robotics
UR - http://www.scopus.com/inward/record.url?scp=85081161757&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081161757&partnerID=8YFLogxK
U2 - 10.1109/IROS40897.2019.8968464
DO - 10.1109/IROS40897.2019.8968464
M3 - Conference contribution
AN - SCOPUS:85081161757
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 4066
EP - 4071
BT - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
T2 - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Y2 - 3 November 2019 through 8 November 2019
ER -