Policy Design for an Ankle-Foot Orthosis Using Simulated Physical Human–Robot Interaction via Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Policy Design for an Ankle-Foot Orthosis Using Simulated Physical Human–Robot Interaction via Deep Reinforcement Learning

Published:2022 Issue: Volume:30 Page:2186-2197
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Han Jong In¹^ORCID,Lee Jeong-Hoon¹^ORCID,Choi Ho Seon²^ORCID,Kim Jung-Hoon³^ORCID,Choi Jongeun¹^ORCID

Affiliation:

1. School of Mechanical Engineering, Yonsei University, Seoul, South Korea

2. Center for Healthcare Robotics, Korea Institute of Science and Technology, Seoul, South Korea

3. School of Civil and Environmental Engineering, Yonsei University, Seoul, South Korea

Funder

National Research Foundation of Korea (NRF) through the Korea Government [Ministry of Science and ICT (MSIT)]

Yonsei-Korea Institute of Science and Technology (KIST) Convergence Research Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

Reference61 articles.

1. Efficient reductions for imitation learning;ross;Proc 13th Int Conf Artif Intell Statist,2010

2. SQIL: Imitation learning via reinforcement learning with sparse rewards;reddy;arXiv 1905 11108,2019

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