Physically interacting individuals estimate the partner's goal to enhance their movements

From a parent helping to guide their child during their first steps, to a therapist supporting a patient, physical assistance enabled by haptic interaction is a fundamental modus for improving motor abilities. However, what movement information is exchanged between partners during haptic interaction, and how this information is used to coordinate and assist others, remains unclear(1). Here, we propose a model in which haptic information, provided by touch and proprioception(2), enables interacting individuals to estimate the partner's movement goal and use it to improve their own motor performance. We use an empirical physical interaction task(3) to show that our model can explain human behaviours better than existing models of interaction in literature(4-8). Furthermore, we experimentally verify our model by embodying it in a robot partner and checking that it induces the same improvements in motor performance and learning in a human individual as interacting with a human partner. These results promise collaborative robots that provide human-like assistance, and suggest that movement goal exchange is the key to physical assistance.