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Functional Electrical Stimulation (FES) is a technique to evoke musclecontraction through low-energy electrical signals. FES can animate paralysedlimbs. Yet, an open challenge remains on how to apply FES to achieve desiredmovements. This challenge is accentuated by the complexities of human bodiesand the non-stationarities of the muscles' responses. The former causesdifficulties in performing inverse dynamics, and the latter causes controlperformance to degrade over extended periods of use. Here, we engage thechallenge via a data-driven approach. Specifically, we learn to control FESthrough Reinforcement Learning (RL) which can automatically customise thestimulation for the patients. However, RL typically has Markovian assumptionswhile FES control systems are non-Markovian because of the non-stationarities.To deal with this problem, we use a recurrent neural network to createMarkovian state representations. We cast FES controls into RL problems andtrain RL agents to control FES in different settings in both simulations andthe real world. The results show that our RL controllers can maintain controlperformances over long periods and have better stimulation characteristics thanPID controllers.

Neuromuscular Reinforcement Learning to Actuate Human Limbs through FES