  Transfer of objects between humans and robots is a critical capability for
collaborative robots. Although there has been a recent surge of interest in
human-robot handovers, most prior research focus on robot-to-human handovers.
Further, work on the equally critical human-to-robot handovers often assumes
humans can place the object in the robot's gripper. In this paper, we propose
an approach for human-to-robot handovers in which the robot meets the human
halfway, by classifying the human's grasp of the object and quickly planning a
trajectory accordingly to take the object from the human's hand according to
their intent. To do this, we collect a human grasp dataset which covers typical
ways of holding objects with various hand shapes and poses, and learn a deep
model on this dataset to classify the hand grasps into one of these categories.
We present a planning and execution approach that takes the object from the
human hand according to the detected grasp and hand position, and replans as
necessary when the handover is interrupted. Through a systematic evaluation, we
demonstrate that our system results in more fluent handovers versus two
baselines. We also present findings from a user study (N = 9) demonstrating the
effectiveness and usability of our approach with naive users in different
scenarios. More results and videos can be found at http://wyang.me/handovers.
