Learning human-to-robot handovers through 3D scene reconstruction

• URL: http://arxiv.org/abs/2507.08726v1
• Date: Fri, 11 Jul 2025 16:26:31 GMT
• Title: Learning human-to-robot handovers through 3D scene reconstruction
• Authors: Yuekun Wu, Yik Lung Pang, Andrea Cavallaro, Changjae Oh,
• Abstract summary: We propose the first method for learning supervised-based 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: Learning robot manipulation policies from raw, real-world image data requires a large number of robot-action trials in the physical environment. Although training using simulations offers a cost-effective alternative, the visual domain gap between simulation and robot workspace remains a major limitation. Gaussian Splatting visual reconstruction methods have recently provided new directions for robot manipulation by generating realistic environments. In this paper, we propose the first method for learning supervised-based robot handovers solely from RGB images without the need of real-robot training or real-robot data collection. The proposed policy learner, Human-to-Robot Handover using Sparse-View Gaussian Splatting (H2RH-SGS), 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. We train a robot policy on demonstrations collected with 16 household objects and {\em directly} deploy this policy in the real environment. Experiments in both Gaussian Splatting reconstructed scene and real-world human-to-robot handover experiments demonstrate that H2RH-SGS serves as a new and effective representation for the human-to-robot handover task.

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