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This paper uses LLM agents (GPT, DeepSeek, GLM, Kimi) to generate and revise exoskeleton control code, closing the loop with human feedback and sensor data. The reported results are a 29.4% and 35.2% drop in biceps and triceps activation across three tasks, with one refinement iteration sufficing for all four models. The authors also note limited adaptability to unexpected situations, so robustness and safety remain open problems. For AI readers, the notable part is the agentic generate-evaluate-revise pattern applied to a physical human-in-the-loop system, not the exoskeleton hardware itself.
Recursive Self-Improvement of Exoskeleton Assistance Policies through Human-in-the-Loop Feedback
arXiv:2611.01581v1 Announce Type: new Abstract: 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.
Abstract: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.
Robotics (cs.RO)
arXiv:2611.01581 [cs.RO]
(or arXiv:2611.01581v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.01581 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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