Toward Human-Robot Teaming: Learning Handover Behaviors from 3D Scenes

• URL: http://arxiv.org/abs/2508.09855v1
• Date: Wed, 13 Aug 2025 14:47:31 GMT
• Title: Toward Human-Robot Teaming: Learning Handover Behaviors from 3D Scenes
• Authors: Yuekun Wu, Yik Lung Pang, Andrea Cavallaro, Changjae Oh,
• Abstract summary: We introduce a method for training HRT policies, focusing on human-to-robot handovers, solely from RGB images.<n>We generate robot demonstrations containing image-action pairs captured with a camera mounted on the robot gripper.<n>As a result, the simulated camera pose changes in the reconstructed scene can be directly translated into gripper pose changes.
• Score: 28.930178662944446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-robot teaming (HRT) systems often rely on large-scale datasets of human and robot interactions, especially for close-proximity collaboration tasks such as human-robot handovers. Learning robot manipulation policies from raw, real-world image data requires a large number of robot-action trials in the physical environment. Although simulation training offers a cost-effective alternative, the visual domain gap between simulation and robot workspace remains a major limitation. We introduce a method for training HRT policies, focusing on human-to-robot handovers, solely from RGB images without the need for real-robot training or real-robot data collection. The goal is to enable the robot to reliably receive objects from a human with stable grasping while avoiding collisions with the human hand. The proposed policy learner leverages sparse-view Gaussian Splatting reconstruction of human-to-robot handover scenes to generate robot demonstrations containing image-action pairs captured with a camera mounted on the robot gripper. As a result, the simulated camera pose changes in the reconstructed scene can be directly translated into gripper pose changes. Experiments in both Gaussian Splatting reconstructed scene and real-world human-to-robot handover experiments demonstrate that our method serves as a new and effective representation for the human-to-robot handover task, contributing to more seamless and robust HRT.

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