Towards An Optimal Strategy for Haptic Dexterous Skill Learning: Development of AI-Driven Adaptive Assistance

Learning a skill is a complex cognitive task that requires processing multiple sensorimotor information. To support the skill learners, in this study, we explored and compared how to exploit AI wisely to teach skills. We selected engraving as the target skill and implemented it in a haptic-audio-visual (HAV) VR environment. As the haptic device, we used a desktop force-feedback device to render the movement and force profile of the tooltip. Starting from gathering experts’ motion and force profile data, we designed a Long Short-Term Memory (LSTM)-based AI model that discriminates the user's behavior and status of the tooltip using the acquired data. With the VR environment and the AI model, we evaluated four teaching strategies for haptic dexterous skill transfer– Guidance, Disturbance, Hybrid , and Adaptive feedback . Guidance feedback provides force cues to help the users, whereas Disturbance applies force that prevents the user's movement. Hybrid alters its mode between Guidance and Disturbance based on the user's performance. Lastly, Adaptive feedback modulates the threshold for altering the mode of Hybrid based on the user's history. We conducted a user experiment with seven sessions: one pre-test, three training sessions, one immediate retention, and two delayed retention sessions. In the results, we found: 1) Guidance showed a steep learning curve during the training sessions, but the participants lost the learning effect in the retention sessions, 2) the learning with Hybrid was the slowest but remained longer, even showed better performance in delayed retention tests, and 3) Adaptive strategy exhibited superior long-term learning effects compared to Hybrid and further suggested improved performance stability. These results seem to follow both the guidance hypothesis and the phenomenon of offline consolidation in learning, highlighting the importance of designing adaptive strategies in Teaching AI systems for dexterous skill learning.