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Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis by Yue Wen; Jennie Si; Andrea Brandt; Xiang Gao; He Helen Huang is a Computer Science article available to read on EtoBox.

What is Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis about?

Robotic prostheses deliver greater function than passive prostheses, but we face the challenge of tuning a large number of control parameters in order to personalize the device for individual amputee users. This problem is not easily solved by traditional control designs or the latest robotic technology. Reinforcement learning (RL) is naturally appealing. The recent, unprecedented success of AlphaZero demonstrated RL as a feasible, large-scale problem solver. However, the prosthesis-tuning problem is associated with several unaddressed issues such as that it does not have a known and stable model, the continuous states and controls of the problem may result in a curse of dimensionality, and the human-prosthesis system is constantly subject to measurement noise, environmental change and human-body-caused variations. In this paper, we demonstrated the feasibility of direct heuristic dynamic programming, an approximate dynamic programming (ADP) approach, to automatically tune the 12 robotic knee prosthesis parameters to meet individual human users' needs. We tested the ADP-tuner on two subjects (one able-bodied subject and one amputee subject) walking at a fixed speed on a treadmill.

Who reads Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis?

Author

Yue Wen; Jennie Si; Andrea Brandt; Xiang Gao; He Helen Huang

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2019

Language

EN

Field

Computer Science (Physical Sciences)