Learning Hip Exoskeleton Control Policy via Predictive Neuromusculoskeletal Simulation

• URL: http://arxiv.org/abs/2603.04166v1
• Date: Wed, 04 Mar 2026 15:23:50 GMT
• Title: Learning Hip Exoskeleton Control Policy via Predictive Neuromusculoskeletal Simulation
• Abstract summary: We present a physics-based neuromusculoskeletal learning framework that trains a hip-exoskeleton control policy entirely in simulation.<n>A reinforcement learning teacher policy is trained using a muscle-synergy action prior over a wide range of walking speeds and slopes.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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