  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.
