Recursive Self-Improvement of Exoskeleton Assistance Policies through Human-in-the-Loop Feedback
Overview
This research proposes a framework for the autonomous evolution of exoskeleton assistance policies using human feedback. An intelligent agent generates executable control code, evaluates it with human participants incorporating subjective feedback, movement performance, and muscle activation data, and autonomously revises the policy based on identified deficiencies. The system establishes a closed loop of generation, feedback, evaluation, and refinement. Evaluated using GPT, DeepSeek, GLM, and Kimi, all models achieved effective assistance after a single refinement iteration. Across three tasks, biceps and triceps muscle activation levels were reduced by 29.4% and 35.2%, respectively, compared to measurements without an exoskeleton. However, the study observed limited adaptability to unexpected situations, indicating a need for further improvements in robustness and safety.
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Control: reported autonomous
“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 perfo”
Exact source · revision 1Source owner not reported
Reported quantity · number of tasks tested: 3 other · Basis not reported
“istance 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, li”
Exact source · revision 1Source owner not reported
Reported quantity · number of tasks across which assistance is achieved: 3 other · Basis not reported
“istance 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, li”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalRecursive Self-Improvement of Exoskeleton Assistance Policies through Human-in-the-Loop Feedback
This research proposes a framework for the autonomous evolution of exoskeleton assistance policies using human feedback. An intelligent agent generates executable control code, evaluates it with human participants incorporating subjective feedback, movement performance, and muscle activation data, and autonomously revises the policy based on identified deficiencies. The system establishes a closed loop of generation, feedback, evaluation, and refinement. Evaluated using GPT, DeepSeek, GLM, and Kimi, all models achieved effective assistance after a single refinement iteration. Across three tasks, biceps and triceps muscle activation levels were reduced by 29.4% and 35.2%, respectively, compared to measurements without an exoskeleton. However, the study observed limited adaptability to unexpected situations, indicating a need for further improvements in robustness and safety.
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