Collaborative Object Handover in a Robot Crafting Assistant

TL;DR

This paper presents a new collaborative handover model for robots that is trained on human teleoperation data and evaluated through experiments and user studies, aiming to improve safe and efficient human-robot collaboration.

Contribution

It introduces a novel handover strategy trained on naturalistic human data and evaluates its effectiveness in real-world collaborative tasks.

Findings

The autonomous handover policy successfully performed collaborative handovers.

User perceptions of the autonomous system were comparable to human teleoperation.

There are identified areas for further improvement in the handover strategy.

Abstract

Robots are increasingly working alongside people, delivering food to patrons in restaurants or helping workers on assembly lines. These scenarios often involve object handovers between the person and the robot. To achieve safe and efficient human-robot collaboration (HRC), it is important to incorporate human context in a robot's handover strategies. We develop a collaborative handover model trained on human teleoperation data collected in a naturalistic crafting task. To evaluate its performance, we conduct cross-validation experiments on the training dataset as well as a user study in the same HRC crafting task. The handover episodes and user perceptions of the autonomous handover policy were compared with those of the human teleoperated handovers. While the cross-validation experiment and user study indicate that the autonomous policy successfully achieved collaborative handovers, the comparison with human teleoperation revealed avenues for further improvements.