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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.03579 |
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| _version_ | 1866910859702304768 |
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| author | Zhang, Hanxin Dhafer, Abdulqader Hao, Zhou Daniel Dong, Hongbiao |
| author_facet | Zhang, Hanxin Dhafer, Abdulqader Hao, Zhou Daniel Dong, Hongbiao |
| contents | We propose a novel system for robot-to-human object handover that emulates human coworker interactions. Unlike most existing studies that focus primarily on grasping strategies and motion planning, our system focus on 1. inferring human handover intents, 2. imagining spatial handover configuration. The first one integrates multimodal perception-combining visual and verbal cues-to infer human intent. The second one using a diffusion-based model to generate the handover configuration, involving the spacial relationship among robot's gripper, the object, and the human hand, thereby mimicking the cognitive process of motor imagery. Experimental results demonstrate that our approach effectively interprets human cues and achieves fluent, human-like handovers, offering a promising solution for collaborative robotics. Code, videos, and data are available at: https://i3handover.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03579 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Generative System for Robot-to-Human Handovers: from Intent Inference to Spatial Configuration Imagery Zhang, Hanxin Dhafer, Abdulqader Hao, Zhou Daniel Dong, Hongbiao Robotics Machine Learning I.2.9 We propose a novel system for robot-to-human object handover that emulates human coworker interactions. Unlike most existing studies that focus primarily on grasping strategies and motion planning, our system focus on 1. inferring human handover intents, 2. imagining spatial handover configuration. The first one integrates multimodal perception-combining visual and verbal cues-to infer human intent. The second one using a diffusion-based model to generate the handover configuration, involving the spacial relationship among robot's gripper, the object, and the human hand, thereby mimicking the cognitive process of motor imagery. Experimental results demonstrate that our approach effectively interprets human cues and achieves fluent, human-like handovers, offering a promising solution for collaborative robotics. Code, videos, and data are available at: https://i3handover.github.io. |
| title | A Generative System for Robot-to-Human Handovers: from Intent Inference to Spatial Configuration Imagery |
| topic | Robotics Machine Learning I.2.9 |
| url | https://arxiv.org/abs/2503.03579 |