Active Contact Sensing for Robust Robot-to-Human Object Handover

TL;DR

This paper introduces an active sensing method for robot-to-human object handover that uses robot motions and force sensing to reliably detect firm grasp versus incidental touch, improving robustness.

Contribution

The paper presents a novel active sensing approach with a Bayesian model to distinguish contact states, outperforming passive methods in diverse scenarios.

Findings

Achieved 97.5% success rate in experiments with 12 participants and 30 objects.

Over 30% improvement over baseline methods in contact state detection.

Enabled robust, generalizable robot-to-human handovers.

Abstract

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.