Learning Hip Exoskeleton Control Policy via Predictive Neuromusculoskeletal Simulation

2026-03-04

AI Summary

This study addresses the challenge of deploying exoskeleton controllers in real-world settings, where traditional approaches rely heavily on motion capture and biomechanical annotations. The authors propose a physics-based neuromusculoskeletal simulation framework that integrates muscle synergy priors with a two-stage curriculum reinforcement learning approach to train hip exoskeleton control policies entirely in simulation. By leveraging policy distillation, the learned strategy is directly transferred to physical hardware without requiring real-world demonstrations. This work presents the first instance of a controller trained exclusively in simulation and successfully deployed on a physical exoskeleton, achieving generalized performance across multiple walking speeds and slopes. Experiments demonstrate a 3.4% reduction in simulated muscle activation and a 7.0% decrease in joint positive power. Hardware evaluations confirm high policy fidelity (r = 0.82, RMSE = 0.03 Nm/kg), validating effective sim-to-real transfer.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Behavior Learning & Control
• 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: LLM based quality controls for crowd work

Abstract

Developing exoskeleton controllers that generalize across diverse locomotor conditions typically requires extensive motion-capture data and biomechanical labeling, limiting scalability beyond instrumented laboratory settings. Here, we present a physics-based neuromusculoskeletal learning framework that trains a hip-exoskeleton control policy entirely in simulation, without motion-capture demonstrations, and deploys it on hardware via policy distillation. A reinforcement learning teacher policy is trained using a muscle-synergy action prior over a wide range of walking speeds and slopes through a two-stage curriculum, enabling direct comparison between assisted and no-exoskeleton conditions. In simulation, exoskeleton assistance reduces mean muscle activation by up to 3.4% and mean positive joint power by up to 7.0% on level ground and ramp ascent, with benefits increasing systematically with walking speed. On hardware, the assistance profiles learned in simulation are preserved across matched speed-slope conditions (r: 0.82, RMSE: 0.03 Nm/kg), providing quantitative evidence of sim-to-real transfer without additional hardware tuning. These results demonstrate that physics-based neuromusculoskeletal simulation can serve as a practical and scalable foundation for exoskeleton controller development, substantially reducing experimental burden during the design phase.

Problem

• exoskeleton control
• generalization
• motion-capture data
• scalability
• locomotor conditions

Innovation

• neuromusculoskeletal simulation
• reinforcement learning
• sim-to-real transfer
• exoskeleton control
• policy distillation