Feasibility Study on Parameter Adjustment for a Humanoid Using LLM Tailoring Physical Care

The increasing demand for care of the elderly, coupled with the shortage of caregivers, necessitates the introduction of robotic assistants capable of performing care tasks both intelligently and safely. Central to these tasks, especially those involving tactile interaction, is the ability to make human-in-the-loop adjustments based on individual preferences. In this study, our primary goal was to design and evaluate a system that captures user preferences prior to initiating a tactile care task. Our focus was on range-of-motion training exercises, emphasizing communication that demonstrates motion using the LLM approach. The system combines physical demonstrations with verbal explanations, ensuring adaptability to individual preferences before initiating range-of-motion training. Using the humanoid robot Dry-AIREC, augmented with the linguistic capabilities of ChatGPT, our system was evaluated with 14 young participants. The results showed that the robot could perform the range-of-motion exercises with tactile interactions while simultaneously communicating with the participant. Thus, our proposed system emerges as a promising approach for range-of-motion exercises rooted in human-preference-centered human-robot interaction. Interestingly, although there wasn’t a significant shift in the overall positive subjective impressions when the tuning was performed using ChatGPT, there was an increase in the number of participants who gave the highest rating to the experience.