Title: Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned Simulation
Primary area: applications to robotics, autonomy, planning
Keywords: Deep reinforcement learning; Musculoskeletal simulation; Pathological gait generalization; Sim-to-real matching

Abstract: Exoskeletons show great promise for enhancing mobility, but providing appropriate assistance remains challenging due to the complexity of human adaptation to external forces. Current state-of-the-art approaches for optimizing exoskeleton controllers require extensive human experiments in which participants must walk for hours, creating a paradox: those who could benefit most from exoskeleton assistance, such as individuals with mobility impairments, are often unable to participate in such demanding procedures. We present Exo-plore, a simulation framework that combines neuromechanical simulation with deep reinforcement learning to optimize hip exoskeleton assistance without requiring real human experiments. Exo-plore can (1) generate realistic gait data that captures human adaptation to assistive forces, (2) produce reliable optimization results despite the stochastic nature of human gait, and (3) generalize to pathological gaits, showing strong linear relationships between pathology severity and optimal assistance. Please refer to the following link for supplemental materials https://iclr2026anonymous.github.io

TL;DR: A Deep RL-based musculoskeletal simulation framework that optimizes exoskeleton parameters to reduce human metabolic cost.