Robotic Knee Prosthesis Real-Time Control Using Reinforcement Learning with Human in the Loop

Yue Wen, Xiang Gao, Jennie Si, Andrea Brandt, Minhan Li, He (Helen) Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Advanced robotic prostheses are expensive considering the cost of human resources and the time spent on manually tuning the high-dimensional control parameters for individual users. To alleviate clinicians’ effort and promote the advanced robotic prosthesis, we implemented an optimal adaptive control algorithm, which fundamentally is a type of reinforcement learning method, to automatically tune the high-dimensional control parameters of a robotic knee prosthesis through interaction with a human-prosthesis system. The ‘human-in-the-loop’ term means that the learning controller tunes the control parameters based on the performance of the robotic knee prosthesis while an amputee subject walking with it. We validated the human-in-the-loop auto-tuner with one transfemoral amputee subject for 4 hour-long lab testing sessions. Our results demonstrated that this novel reinforcement learning controller was able to learn through interaction with the human-prosthesis system and discover a set of suitable control parameter for the amputee user to generate near-normative knee kinematics.

Original languageEnglish (US)
Title of host publicationCognitive Systems and Signal Processing - 4th International Conference, ICCSIP 2018, Revised Selected Papers
EditorsFuchun Sun, Huaping Liu, Dewen Hu
PublisherSpringer Verlag
Pages463-473
Number of pages11
ISBN (Print)9789811379826
DOIs
StatePublished - Jan 1 2019
Event4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018 - Beijing, China
Duration: Nov 29 2018Dec 1 2018

Publication series

NameCommunications in Computer and Information Science
Volume1005
ISSN (Print)1865-0929

Conference

Conference4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018
CountryChina
CityBeijing
Period11/29/1812/1/18

Keywords

  • Amputees
  • Gait symmetry
  • Machine learning
  • Prosthetic knee kinematics
  • Reinforcement learning
  • Robotic knee prosthesis

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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