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arXiv · 2508.09855

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

Yuekun Wu·Yik Lung Pang·Andrea Cavallaro·Changjae Oh
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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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Yuekun Wu, Yik Lung Pang, Andrea Cavallaro, Changjae Oh. 2025-08-13. Toward Human-Robot Teaming: Learning Handover Behaviors from 3D Scenes. https://arxiv.org/abs/2508.09855

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