Reinforcement Learning Control Outperforms Iterative Learning in Exoskeleton-Assisted Gait Training

Abstract

Learning-based controllers are increasingly adopted in lower-extremity powered exoskeletons, yet their advantages over traditional adaptive approaches remain underexplored. We compared two adaptive assist-as-needed (AAN) controllers for gait training with an ankle exoskeleton: a reinforcement learning-based controller (RL-AAN) and a conventional iterative learning controller (ILC-AAN). Both adjusted assistance stride-by-stride, delivering torque as a percentage of the wearer's biological plantarflexion moment—estimated online with a subject-agnostic model—and progressively faded assistance as performance improved. Healthy participants walked on a self-paced treadmill under a perturbed-gait protocol. Performance was assessed as average percent stride-velocity (SV) improvement relative to unassisted perturbed walking (Δ%ε SV + ) and percent of strides above the SV threshold (N% SV + ). During training, RL-AAN and ILC-AAN elicited comparable gains in Δ%ε SV + between the first and last training sessions, but RL-AAN yielded greater adherence across sessions, as indicated by larger N% SV + . After training, RL-AAN demonstrated superior retention in Δ%ε SV + and N% SV + . These results support RL-AAN as a promising strategy for subject-tailored gait training, motivating future studies in neurological and musculoskeletal populations.