智能体通过人类反馈自动迭代外骨骼辅助策略
Recursive Self-Improvement of Exoskeleton Assistance Policies through Human-in-the-Loop Feedback
用GPT、DeepSeek等四个模型自动写外骨骼控制代码，靠真人反馈迭代，肌肉激活降了三成左右，思路挺新鲜。
一篇arXiv论文提出通过人类反馈实现外骨骼辅助策略自主进化的框架，由智能体自动生成可执行的控制代码并用真人实验评估，评估依据包括用户主观反馈、运动表现和肌肉激活数据。框架用GPT、DeepSeek、GLM和Kimi四个模型测试，经过一次自主迭代后四个模型均能实现有效辅助。与不穿外骨骼相比，三项任务中肱二头肌和肱三头肌的肌肉激活水平分别降低29.4%和35.2%。论文同时指出该框架对意外情况的适应性有限，鲁棒性和安全性仍需改进。
Recursive Self-Improvement of Exoskeleton Assistance Policies through Human-in-the-Loop Feedback
To reduce reliance on manual design and extensive parameter tuning, a framework for the autonomous evolution of exoskeleton assistance policies through human feedback is proposed. Assistance policies are autonomously generated as executable control code by an intelligent agent and evaluated in experiments with human participants. Subjective user feedback, movement performance, and muscle activation measurements are incorporated into policy assessment. Based on these assessments, policy deficiencies are autonomously identified and the control code is revised by the agent for subsequent trials. A closed loop of policy generation, human feedback, evaluation, and autonomous refinement is thereby established. The framework is evaluated using GPT, DeepSeek, GLM, and Kimi, and initial assistance performance and refinement outcomes are compared across models. Despite differences in initial performance, effective assistance is achieved with all four models after a single autonomous refinement iteration under the tested conditions. Across three tasks, biceps and triceps muscle activation levels are reduced by 29.4% and 35.2%, respectively, compared with those measured without an exoskeleton. However, limited adaptability to unexpected situations is observed, indicating that further improvements in robustness and safety are required.
