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This paper introduces an active contact sensing approach for robot-to-human object handover, where the robot applies small motions to infer the contact state based on human-applied forces. A Bayesian linear model is used to represent the relationship between robot motions and human forces, distinguishing firm grasps from incidental touches. Experiments across diverse objects and human participants demonstrate a 97.5% success rate, significantly outperforming passive sensing baselines.
Robots can reliably hand over objects to humans by actively probing grasps, achieving a 30% improvement over passive methods.
Robot-to-human object handover is an essential skill for robot assistants, from serving drinks at home to passing surgical tools in the operating room. We expect robots to perform handover robustly -- to release the object only after a firm human grasp while ignoring incidental touches. Existing passive-sensing methods struggle to generalize across diverse objects and human behaviors, as they lack informative perturbations to disambiguate different contact conditions, such as firm grasp versus incidental touch. We propose an active sensing approach for robust handovers: the robot applies information-gathering motions and senses the resulting human-applied forces to infer the contact state. A firm grasp produces forces in multiple directions, while an accidental touch does not. To capture this distinction, we model the contact state with a Bayesian linear model: a distribution over piecewise-linear mappings from robot motions to human-applied forces. This model enables firm grasp detection and active information gathering. In experiments with 12 participants and 30 diverse rigid objects, our method achieved a 97.5% success rate -- over 30% higher than two common baselines.
