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Synthetic Data-Driven Exoskeleton Control via Contralateral Gait Fusion for Variable-Speed Walking.

Jingshu Shi¹, Hongwu Zhu², Yifei Yang¹

¹Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.

Biomimetics (Basel, Switzerland)

|May 26, 2026

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This study introduces a synthetic data generation method using an Adversarial Motion Priors (AMP) agent to create data-driven exoskeletons. This approach enables real-time, adaptive torque assistance for variable-speed walking, reducing user effort and improving safety.

Area of Science:

Robotics
Biomechanics
Machine Learning

Background:

Data-driven exoskeletons promise enhanced human mobility but face challenges in data collection and tuning.
Current methods require extensive manual effort, limiting widespread adoption.

Purpose of the Study:

To develop an efficient synthetic data generation pipeline for data-driven exoskeleton control.
To enable real-time, adaptive torque assistance for variable-speed walking.

Main Methods:

Leveraged an Adversarial Motion Priors (AMP) agent in physics-based simulation to generate high-fidelity walking data.
Developed a CNN-Transformer architecture for end-to-end torque prediction from contralateral swing-phase data.
Validated the approach using a custom ankle exoskeleton for sim-to-real transferability.

Keywords:

contralateral gait data-driven exoskeleton reinforcement learning sim-to-real transfer

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Main Results:

Achieved low prediction error (RMSE ~0.081 Nm/kg, R² ~0.836) across various walking speeds (0.6-1.75 m/s).
Demonstrated significant reduction in user ankle mechanical work (up to 14%).
Showcased inherent fault tolerance in the multi-sensor configuration for safe operation.

Conclusions:

The synthetic data approach provides a scalable pathway for autonomous exoskeleton deployment.
This method facilitates practical, real-world application of adaptive exoskeleton assistance.
The system offers robust performance and enhanced safety features.