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

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 6DoF 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. More results can be found on the project website.

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Publication Details

Published
2024-10-14
DOI
https://doi.org/10.1109/iros58592.2024.10801777
Citations
9
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
2.95
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article

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

Shuran Song, Zeyi Liu, Nicolas Ouporov, Zixi Wang
9 citations
Robot Manipulation and Learning
2.95
article

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

Shuran Song, Zeyi Liu, Nicolas Ouporov, Zixi Wang
article en
9 citations

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 6DoF 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. More results can be found on the project website.

Columbia University (US), Stanford University (US)
Reduced inequalities
Openalex Percentile: Top 8%
Robot Manipulation and Learning
2.95
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