Target-Oriented Object Grasping via Multimodal Human Guidance
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914917989220352 |
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| author | Xie, Pengwei Chen, Siang Hu, Dingchang Dai, Yixiang Yang, Kaiqin Wang, Guijin |
| author_facet | Xie, Pengwei Chen, Siang Hu, Dingchang Dai, Yixiang Yang, Kaiqin Wang, Guijin |
| contents | In the context of human-robot interaction and collaboration scenarios, robotic grasping still encounters numerous challenges. Traditional grasp detection methods generally analyze the entire scene to predict grasps, leading to redundancy and inefficiency. In this work, we reconsider 6-DoF grasp detection from a target-referenced perspective and propose a Target-Oriented Grasp Network (TOGNet). TOGNet specifically targets local, object-agnostic region patches to predict grasps more efficiently. It integrates seamlessly with multimodal human guidance, including language instructions, pointing gestures, and interactive clicks. Thus our system comprises two primary functional modules: a guidance module that identifies the target object in 3D space and TOGNet, which detects region-focal 6-DoF grasps around the target, facilitating subsequent motion planning. Through 50 target-grasping simulation experiments in cluttered scenes, our system achieves a success rate improvement of about 13.7%. In real-world experiments, we demonstrate that our method excels in various target-oriented grasping scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11138 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Target-Oriented Object Grasping via Multimodal Human Guidance Xie, Pengwei Chen, Siang Hu, Dingchang Dai, Yixiang Yang, Kaiqin Wang, Guijin Robotics Computer Vision and Pattern Recognition In the context of human-robot interaction and collaboration scenarios, robotic grasping still encounters numerous challenges. Traditional grasp detection methods generally analyze the entire scene to predict grasps, leading to redundancy and inefficiency. In this work, we reconsider 6-DoF grasp detection from a target-referenced perspective and propose a Target-Oriented Grasp Network (TOGNet). TOGNet specifically targets local, object-agnostic region patches to predict grasps more efficiently. It integrates seamlessly with multimodal human guidance, including language instructions, pointing gestures, and interactive clicks. Thus our system comprises two primary functional modules: a guidance module that identifies the target object in 3D space and TOGNet, which detects region-focal 6-DoF grasps around the target, facilitating subsequent motion planning. Through 50 target-grasping simulation experiments in cluttered scenes, our system achieves a success rate improvement of about 13.7%. In real-world experiments, we demonstrate that our method excels in various target-oriented grasping scenarios. |
| title | Target-Oriented Object Grasping via Multimodal Human Guidance |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.11138 |