Hand by Hand: LLM Driving EMS Assistant for Operational Skill Learning

August 08, 2025 Β· Declared Dead Β· πŸ› International Joint Conference on Artificial Intelligence

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Authors Wei Xiang, Ziyue Lei, Haoyuan Che, Fangyuan Ye, Xueting Wu, Lingyun Sun arXiv ID 2508.06000 Category cs.HC: Human-Computer Interaction Cross-listed cs.AI Citations 0 Venue International Joint Conference on Artificial Intelligence Last Checked 4 months ago
Abstract
Operational skill learning, inherently physical and reliant on hands-on practice and kinesthetic feedback, has yet to be effectively replicated in large language model (LLM)-supported training. Current LLM training assistants primarily generate customized textual feedback, neglecting the crucial kinesthetic modality. This gap derives from the textual and uncertain nature of LLMs, compounded by concerns on user acceptance of LLM driven body control. To bridge this gap and realize the potential of collaborative human-LLM action, this work explores human experience of LLM driven kinesthetic assistance. Specifically, we introduced an "Align-Analyze-Adjust" strategy and developed FlightAxis, a tool that integrates LLM with Electrical Muscle Stimulation (EMS) for flight skill acquisition, a representative operational skill domain. FlightAxis learns flight skills from manuals and guides forearm movements during simulated flight tasks. Our results demonstrate high user acceptance of LLM-mediated body control and significantly reduced task completion times. Crucially, trainees reported that this kinesthetic assistance enhanced their awareness of operation flaws and fostered increased engagement in the training process, rather than relieving perceived load. This work demonstrated the potential of kinesthetic LLM training in operational skill acquisition.
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