ROSYJan 22, 2021

Robotic Knee Tracking Control to Mimic the Intact Human Knee Profile Based on Actor-critic Reinforcement Learning

arXiv:2101.09334v1
Originality Incremental advance
AI Analysis

This work addresses improved mobility for transfemoral amputees by advancing from regulation control to tracking control, though it is incremental as it builds on previous RL-based tuning methods.

The paper tackled the problem of enabling robotic knee prostheses to mimic intact human knee profiles for transfemoral amputees, using an actor-critic reinforcement learning approach based on direct heuristic dynamic programming, and demonstrated its effectiveness through simulations showing level ground walking, varied terrains, and paces.

We address a state-of-the-art reinforcement learning (RL) control approach to automatically configure robotic prosthesis impedance parameters to enable end-to-end, continuous locomotion intended for transfemoral amputee subjects. Specifically, our actor-critic based RL provides tracking control of a robotic knee prosthesis to mimic the intact knee profile. This is a significant advance from our previous RL based automatic tuning of prosthesis control parameters which have centered on regulation control with a designer prescribed robotic knee profile as the target. In addition to presenting the complete tracking control algorithm based on direct heuristic dynamic programming (dHDP), we provide an analytical framework for the tracking controller with constrained inputs. We show that our proposed tracking control possesses several important properties, such as weight convergence of the learning networks, Bellman (sub)optimality of the cost-to-go value function and control input, and practical stability of the human-robot system under input constraint. We further provide a systematic simulation of the proposed tracking control using a realistic human-robot system simulator, the OpenSim, to emulate how the dHDP enables level ground walking, walking on different terrains and at different paces. These results show that our proposed dHDP based tracking control is not only theoretically suitable, but also practically useful.

Foundations

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