Click to Grasp: Zero-Shot Precise Manipulation via Visual Diffusion Descriptors
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866909440624558080 |
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| author | Tsagkas, Nikolaos Rome, Jack Ramamoorthy, Subramanian Mac Aodha, Oisin Lu, Chris Xiaoxuan |
| author_facet | Tsagkas, Nikolaos Rome, Jack Ramamoorthy, Subramanian Mac Aodha, Oisin Lu, Chris Xiaoxuan |
| contents | Precise manipulation that is generalizable across scenes and objects remains a persistent challenge in robotics. Current approaches for this task heavily depend on having a significant number of training instances to handle objects with pronounced visual and/or geometric part ambiguities. Our work explores the grounding of fine-grained part descriptors for precise manipulation in a zero-shot setting by utilizing web-trained text-to-image diffusion-based generative models. We tackle the problem by framing it as a dense semantic part correspondence task. Our model returns a gripper pose for manipulating a specific part, using as reference a user-defined click from a source image of a visually different instance of the same object. We require no manual grasping demonstrations as we leverage the intrinsic object geometry and features. Practical experiments in a real-world tabletop scenario validate the efficacy of our approach, demonstrating its potential for advancing semantic-aware robotics manipulation. Web page: https://tsagkas.github.io/click2grasp |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14526 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Click to Grasp: Zero-Shot Precise Manipulation via Visual Diffusion Descriptors Tsagkas, Nikolaos Rome, Jack Ramamoorthy, Subramanian Mac Aodha, Oisin Lu, Chris Xiaoxuan Robotics Artificial Intelligence Computer Vision and Pattern Recognition Precise manipulation that is generalizable across scenes and objects remains a persistent challenge in robotics. Current approaches for this task heavily depend on having a significant number of training instances to handle objects with pronounced visual and/or geometric part ambiguities. Our work explores the grounding of fine-grained part descriptors for precise manipulation in a zero-shot setting by utilizing web-trained text-to-image diffusion-based generative models. We tackle the problem by framing it as a dense semantic part correspondence task. Our model returns a gripper pose for manipulating a specific part, using as reference a user-defined click from a source image of a visually different instance of the same object. We require no manual grasping demonstrations as we leverage the intrinsic object geometry and features. Practical experiments in a real-world tabletop scenario validate the efficacy of our approach, demonstrating its potential for advancing semantic-aware robotics manipulation. Web page: https://tsagkas.github.io/click2grasp |
| title | Click to Grasp: Zero-Shot Precise Manipulation via Visual Diffusion Descriptors |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.14526 |