Virtual Impedance Adaptation of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning Article Swipe

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Ranran Zheng , Zhiyuan Yu , Hongwei Liu , Junqin Lin , Bo Zeng , Longfei Jia · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1186/s10033-025-01355-y

This paper proposes virtual impedance adaptation of the lower-limb exoskeleton for human performance augmentation (LEHPA) based on deep reinforcement learning (VIADRL) to mitigate reliance on model accuracy and address the ever-changing human-exoskeleton interaction (HEI) dynamics. The classical sensitivity amplification control strategy is expanded to the virtual impedance control strategy with more learnable virtual impedance parameters. The adjustment of these virtual impedance parameters is formalized as finding the optimal policy for a Markov Decision Process and can then be effectively resolved using deep reinforcement learning algorithms. To ensure safe and efficient policy training, a multibody simulation environment is established to facilitate the training process, supplemented by the innovative hybrid inverse-forward dynamics simulation approach for executing the simulation. For comparison purposes, the SADRL strategy is introduced as a benchmark. A novel control performance evaluation method based on the HEI forces at the back, thighs, and shanks is proposed to quantitatively evaluate the performance of our proposed VIADRL strategy. The VIADRL controller is systematically compared with the SADRL controller at five selected walking speeds. The lumped ratio of HEI forces under the SADRL strategy relative to those under the SADRL strategy is as low as 0.81 in simulation and approximately 0.89 on the LEHPA prototype. The overall reduction of HEI forces demonstrates the superiority of the VIADRL strategy in comparison to the SADRL strategy.

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article

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en

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https://doi.org/10.1186/s10033-025-01355-y

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Virtual Impedance Adaptation of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

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article

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2025

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2025-10-09

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Ranran Zheng, Zhiyuan Yu, Hongwei Liu, Junqin Lin, Bo Zeng, Longfei Jia

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https://doi.org/10.1186/s10033-025-01355-y

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