Ensuring Interaction Safety in Multitask Exoskeleton Control: A Simulation-Trained Variable Impedance Framework

2026-06-04

AI Summary

This work addresses the challenge of simultaneously achieving cross-task adaptability and human-robot interaction safety in multi-task exoskeleton control. To this end, the authors propose a bimodal variable impedance control framework that integrates semantic commands with proprioceptive history. Leveraging reinforcement learning, human-exoskeleton collaboration data are generated in simulation to enable end-to-end training of a policy network that jointly predicts reference trajectories and impedance parameters. Notably, Lyapunov stability constraints are explicitly embedded into the learning process for the first time, guaranteeing asymptotic system stability. Experimental results demonstrate that the proposed framework significantly reduces users’ metabolic cost in real-world scenarios and outperforms baseline methods, thereby validating its effectiveness in balancing performance and safety across diverse tasks.

Abstract

Wearable exoskeletons can augment human phys ical capabilities during complex activities. However, ensuring adaptation across diverse tasks while guaranteeing interaction safety remains a critical challenge. To address this, a simulation trained variable impedance control approach with stability guarantees is proposed. First, a simulation-based human exoskeleton motion data generation pipeline is established, utilizing Proximal Policy Optimization (PPO) to synthesize human muscle activations while the exoskeleton provides direct compensation for human biological joint torques. Subsequently, the generated dataset is used to train a dual modality policy that fuses semantic instructions with proprioceptive history, enabling the prediction of reference trajectories and variable impedance gains for nine different motion tasks. To guarantee safety, the network outputs are constrained by a stability criterion derived from Lyapunov stability theory, which bounds stiffness variations to ensure the asymptotic stability of the coupled system. Experimental results indicate that the proposed framework reduces metabolic cost in real-world scenarios com pared with standard baseline methods. These findings suggest the feasibility of the proposed framework for safe, multitask exoskeleton control.

Problem

• exoskeleton control
• interaction safety
• multitask adaptation
• variable impedance
• human-robot interaction

Innovation

• variable impedance control
• simulation-trained policy
• Lyapunov stability
• dual-modality fusion
• exoskeleton safety