  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.
