Exoskeleton Control through Learning to Reduce Biological Joint Moments in Simulations
Data-driven joint-moment predictors offer a scalable alternative to laboratory-based inverse-dynamics pipelines for biomechanics estimation and exoskeleton control. Meanwhile, physics-based reinforcement learning (RL) enables simulation-trained controllers to learn dynamics-aware assistance strategies without extensive human experimentation. However, quantitative verification of simulation-trained exoskeleton torque predictors, and their impact on human joint power injection, remains limited. This paper presents (1) an RL framework to learn exoskeleton assistance policies that reduce biological joint moments, and (2) a validation pipeline that verifies the trained control networks using a...
| Authors | Zihang You, Xianlian Zhou |
|---|---|
| Primary category | cs.RO |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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