用仿真训练可变阻抗控制，让外骨骼安全适配多种动作
Ensuring Interaction Safety in Multitask Exoskeleton Control: A Simulation-Trained Variable Impedance Framework
- 通过仿真生成人体肌肉激活数据，训练融合指令与感知的历史策略
- 在9种动作中降低代谢成本，且通过李雅普诺夫理论保证系统稳定
- 适合需要多任务安全控制的外骨骼研发人员参考
可穿戴外骨骼能在复杂活动中增强人体物理能力，但如何在多样任务间实现自适应并确保交互安全仍是关键挑战。为此，提出一种基于仿真的可变阻抗控制方法，具备稳定性保障。首先，构建基于近端策略优化（PPO）的仿真人体-外骨骼运动数据生成流程，模拟人体肌肉激活，并由外骨骼直接补偿生物关节力矩。随后，利用生成的数据集训练双模态策略，融合语义指令与本体感知历史，预测九种不同运动任务的参考轨迹与可变阻抗增益。为保障安全，网络输出受李雅普诺夫稳定性理论导出的稳定性准则约束，限制刚度变化以确保耦合系统的渐近稳定。实验结果表明，该框架在真实场景中相比标准基线方法显著降低代谢成本，验证了其在多任务安全控制中的可行性。
原文摘要 · Abstract (English)
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.
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