  Human-robot handover is a fundamental yet challenging task in human-robot
interaction and collaboration. Recently, remarkable progressions have been made
in human-to-robot handovers of unknown objects by using learning-based grasp
generators. However, how to responsively generate smooth motions to take an
object from a human is still an open question. Specifically, planning motions
that take human comfort into account is not a part of the human-robot handover
process in most prior works. In this paper, we propose to generate smooth
motions via an efficient model-predictive control (MPC) framework that
integrates perception and complex domain-specific constraints into the
optimization problem. We introduce a learning-based grasp reachability model to
select candidate grasps which maximize the robot's manipulability, giving it
more freedom to satisfy these constraints. Finally, we integrate a neural net
force/torque classifier that detects contact events from noisy data. We
conducted human-to-robot handover experiments on a diverse set of objects with
several users (N=4) and performed a systematic evaluation of each module. The
study shows that the users preferred our MPC approach over the baseline system
by a large margin. More results and videos are available at
https://sites.google.com/nvidia.com/mpc-for-handover.
