Robot-to-human object handover is an important step in many human robot
collaboration tasks. A successful handover requires the robot to maintain a
stable grasp on the object while making sure the human receives the object in a
natural and easy-to-use manner. We propose ContactHandover, a robot to human
handover system that consists of two phases: a contact-guided grasping phase
and an object delivery phase. During the grasping phase, ContactHandover
predicts both 6-DoF robot grasp poses and a 3D affordance map of human contact
points on the object. The robot grasp poses are reranked by penalizing those
that block human contact points, and the robot executes the highest ranking
grasp. During the delivery phase, the robot end effector pose is computed by
maximizing human contact points close to the human while minimizing the human
arm joint torques and displacements. We evaluate our system on 27 diverse
household objects and show that our system achieves better visibility and
reachability of human contacts to the receiver compared to several baselines.
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机器人到人的物体交接是许多人机协作任务中的重要一步，我们提出了一种基于接触的机器人到人交接系统 ContactHandover，它由抓取阶段和交付阶段组成，通过预测机器人抓取姿势和对象上人体接触点的三维可行性图，最大化接触点接近人类并最小化人体关节扭矩和位移，以实现更好的接触可见性和可及性。