Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned Simulation

2026-01-30

AI Summary

This study addresses the challenge of optimizing exoskeleton controllers, which typically relies on extensive and time-consuming human trials that are often infeasible for individuals with mobility impairments. To overcome this limitation, the authors propose a novel framework integrating neuromechanical simulation with deep reinforcement learning, enabling, for the first time, the optimization of hip exoskeleton assistance strategies without requiring real human participants. The approach simulates users’ adaptation to assistive forces and generates high-fidelity gait data to stably optimize control policies. The resulting strategies successfully generalize across pathological gaits of varying severity and reveal a linear relationship between impairment severity and optimal assistance magnitude. This work establishes a new paradigm for personalized rehabilitation exoskeleton design by enabling efficient, simulation-based controller development tailored to individual patient needs.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

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 rarely able 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.

Problem

• exoskeleton control
• human adaptation
• mobility impairment
• controller optimization
• assistive robotics

Innovation

• exoskeleton control
• neuromechanical simulation
• deep reinforcement learning
• gait adaptation
• pathological gait