6 citations · 13 across the 4 of their papers we have counts for
5 papers
Reinforcement Learning Enabled Automatic Impedance Control of a Robotic Knee Prosthesis to Mimic the Intact Knee Motion in a Co-Adapting Environment
Ruofan Wu, Minhan Li, Zhikai Yao +3
Automatically configuring a robotic prosthesis to fit its user's needs and physical conditions is a great technical challenge and a roadblock to the adoption of the technology. Pre…
Toward Reliable Designs of Data-Driven Reinforcement Learning Tracking Control for Euler-Lagrange Systems
Zhikai Yao, Jennie Si, Ruofan Wu +1
This paper addresses reinforcement learning based, direct signal tracking control with an objective of developing mathematically suitable and practically useful design approaches.…
A Data-Driven Reinforcement Learning Solution Framework for Optimal and Adaptive Personalization of a Hip Exoskeleton
Xikai Tu, Minhan Li, Ming Liu +3
Robotic exoskeletons are exciting technologies for augmenting human mobility. However, designing such a device for seamless integration with the human user and to assist human move…
Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven
Qingtao Zhao, Jennie Si, Jian Sun
In this paper time-driven learning refers to the machine learning method that updates parameters in a prediction model continuously as new data arrives. Among existing approximate…
Reinforcement Learning Control of Robotic Knee with Human in the Loop by Flexible Policy Iteration
Xiang Gao, Jennie Si, Yue Wen +3
We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees such as stability…