PrioriTouch: Adapting to User Contact Preferences for Whole-Arm Physical Human-Robot Interaction

Abstract

Many robot caregiving tasks, such as bathing, dressing, and transferring, require a robot arm to make contact with a human body at multiple points rather than solely at the end effector. However, varied human touch preferences can lead to unsafe or uncomfortable multi-contact interactions. To address this, we introduce PrioriTouch, a framework integrating a novel contextual bandit algorithm with hierarchical operational space control to learn user contact preferences and translate them into low-level pose and force control policies. PrioriTouch minimizes user discomfort by initially gathering real-world feedback and subsequently refining the policy using simulation-in-the-loop, thus avoiding unsafe user experimentation. Guided by insights from a user study on physical assistance preferences, we rigorously evaluate PrioriTouch in extensive simulation and real-world experiments, demonstrating effective adaptation to user contact preferences, maintained task performance, and enhanced safety and comfort.