Reactive Human-to-Robot Handovers of Arbitrary Objects

TL;DR

This paper introduces a vision-based, reactive system for human-to-robot handovers that can handle diverse, unknown objects with varying appearance, size, shape, and rigidity, ensuring smooth and robust transfers.

Contribution

It presents a novel system combining closed-loop motion planning with real-time grasp generation for arbitrary object handovers, demonstrating high robustness and generalizability.

Findings

Successfully handled 26 diverse household objects

Achieved smooth and reactive handovers in user study

System is robust to object position and orientation variations

Abstract

Human-robot object handovers have been an actively studied area of robotics over the past decade; however, very few techniques and systems have addressed the challenge of handing over diverse objects with arbitrary appearance, size, shape, and rigidity. In this paper, we present a vision-based system that enables reactive human-to-robot handovers of unknown objects. Our approach combines closed-loop motion planning with real-time, temporally-consistent grasp generation to ensure reactivity and motion smoothness. Our system is robust to different object positions and orientations, and can grasp both rigid and non-rigid objects. We demonstrate the generalizability, usability, and robustness of our approach on a novel benchmark set of 26 diverse household objects, a user study with naive users (N=6) handing over a subset of 15 objects, and a systematic evaluation examining different ways of handing objects. More results and videos can be found at https://sites.google.com/nvidia.com/handovers-of-arbitrary-objects.