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Computer Science > Robotics

arXiv:2404.01402 (cs)
[Submitted on 1 Apr 2024 (v1), last revised 30 Sep 2024 (this version, v2)]

Title:ContactHandover: Contact-Guided Robot-to-Human Object Handover

Authors:Zixi Wang, Zeyi Liu, Nicolas Ouporov, Shuran Song
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Abstract: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 re-ranked 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. More results can be found on this https URL
Comments: Accepted to IROS 2024. Project website: this https URL
Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2404.01402 [cs.RO]
  (or arXiv:2404.01402v2 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2404.01402

Submission history

From: Zixi Wang [view email]
[v1] Mon, 1 Apr 2024 18:12:09 UTC (2,923 KB)
[v2] Mon, 30 Sep 2024 07:34:17 UTC (2,916 KB)
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