Toward Expedited Impedance Tuning of a Robotic Prosthesis for Personalized Gait Assistance by Reinforcement Learning Control

Minhan Li, Yue Wen, Xiang Gao, Jennie Si, He Huang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Personalizing medical devices such as lower limb wearable robots is challenging. While the initial feasibility of automating the process of knee prosthesis control parameter tuning has been demonstrated in a principled way, the next critical issue is to improve tuning efficiency and speed it up for the human user, in clinic settings, while maintaining human safety. We, therefore, propose a policy iteration with constraint embedded (PICE) method as an innovative solution to the problem under the framework of reinforcement learning. Central to PICE is the use of a projected Bellman equation with a constraint of assuring positive semidefiniteness of performance values during policy evaluation. Additionally, we developed both online and offline PICE implementations that provide additional flexibility for the designer to fully utilize measurement data, either from on-policy or off-policy, to further improve PICE tuning efficiency. Our human subject testing showed that the PICE provided effective policies with significantly reduced tuning time. For the first time, we also experimentally evaluated and demonstrated the robustness of the deployed policies by applying them to different tasks and users. Putting it together, our new way of problem solving has been effective as PICE has demonstrated its potential toward truly automating the process of control parameter tuning for robotic knee prosthesis users.

Original languageEnglish (US)
JournalIEEE Transactions on Robotics
DOIs
StateAccepted/In press - 2021
Externally publishedYes

Keywords

  • Impedance
  • Impedance control
  • Kinematics
  • Knee
  • knee prosthesis
  • Legged locomotion
  • policy iteration
  • Prosthetics
  • rehabilitation robotics
  • reinforcement learning (RL)
  • Robustness
  • Tuning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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