Sensing the Partner: Toward Effective Robot Tutoring in Motor Skill Learning

Abstract

Effective tutoring during motor learning requires to provide the appropriate physical assistance to the learners, but at the same time to assess and adapt to their state, to avoid frustration. With the aim of endowing robot tutors with these abilities, we designed an experiment in which participants had to acquire a new motor ability - balancing an unstable inverted pendulum - with the support of a robot providing fixed physical assistance. We analyzed participants’ behavior and explicit evaluations to (i) identify the motor strategy associated with best performances in the task; (ii) assess whether natural facial expressions automatically extracted from cameras during task execution can inform about the participant’s state. The results indicate that the variation and the mean of the wrist velocity are the most relevant in the effective balancing strategy, suggesting that a robot tutor could reorient the attention of the pupil on this parameter to facilitate the learning process. Moreover, facial expressions vary significantly during the task, especially in the dimension of Valence, which decreases with training. Interestingly, only when the robot had an anthropomorphic presence, Valence correlated with the degree of frustration experienced in the task. These findings highlight that both physical behavior and affective signals could be integrated by an autonomous robot to generate adaptive and individualized assistance, mindful both of the learning process and the partner’s affective state.