Musculoskeletal Motion Imitation for Learning Personalized Exoskeleton Control Policy in Impaired Gait

2026-04-10

AI Summary

This study addresses the limitations of existing lower-limb exoskeleton control methods, which often rely on extensive data or iterative parameter tuning and thus struggle to generalize to clinical populations. The authors propose a device-agnostic, personalized control framework that uniquely integrates physiologically plausible musculoskeletal simulation with reinforcement learning to automatically generate gait assistance strategies aligned with human biomechanics—without requiring task-specific parameterization. By unifying the modeling of both healthy and pathological gait patterns, the approach produces asymmetric assistive torques tailored to individual muscle weakness. Simulation results demonstrate that the generated hip and ankle torque profiles closely match those of state-of-the-art human-in-the-loop strategies, significantly reducing metabolic cost across multiple walking speeds and effectively improving energy efficiency and bilateral movement symmetry in simulated pathological gait.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Sampling/Simulation-based Search

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
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Designing generalizable control policies for lower-limb exoskeletons remains fundamentally constrained by exhaustive data collection or iterative optimization procedures, which limit accessibility to clinical populations. To address this challenge, we introduce a device-agnostic framework that combines physiologically plausible musculoskeletal simulation with reinforcement learning to enable scalable personalized exoskeleton assistance for both able-bodied and clinical populations. Our control policies not only generate physiologically plausible locomotion dynamics but also capture clinically observed compensatory strategies under targeted muscular deficits, providing a unified computational model of both healthy and pathological gait. Without task-specific tuning, the resulting exoskeleton control policies produce assistive torque profiles at the hip and ankle that align with state-of-the-art profiles validated in human experiments, while consistently reducing metabolic cost across walking speeds. For simulated impaired-gait models, the learned control policies yield asymmetric, deficit-specific exoskeleton assistance that improves both energetic efficiency and bilateral kinematic symmetry without explicit prescription of the target gait pattern. These results demonstrate that physiologically plausible musculoskeletal simulation via reinforcement learning can serve as a scalable foundation for personalized exoskeleton control across both able-bodied and clinical populations, eliminating the need for extensive physical trials.

Problem

• exoskeleton control
• personalized assistance
• impaired gait
• musculoskeletal simulation
• reinforcement learning

Innovation

• musculoskeletal simulation
• reinforcement learning
• personalized exoskeleton control
• impaired gait
• metabolic cost reduction