Guardado en:
| Autores principales: | , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2501.00510 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915091880869888 |
|---|---|
| author | Wan, Zhaoliang Ling, Yonggen Yi, Senlin Qi, Lu Lee, Wangwei Lu, Minglei Yang, Sicheng Teng, Xiao Lu, Peng Yang, Xu Yang, Ming-Hsuan Cheng, Hui |
| author_facet | Wan, Zhaoliang Ling, Yonggen Yi, Senlin Qi, Lu Lee, Wangwei Lu, Minglei Yang, Sicheng Teng, Xiao Lu, Peng Yang, Xu Yang, Ming-Hsuan Cheng, Hui |
| contents | This paper addresses the scarcity of large-scale datasets for accurate object-in-hand pose estimation, which is crucial for robotic in-hand manipulation within the ``Perception-Planning-Control" paradigm. Specifically, we introduce VinT-6D, the first extensive multi-modal dataset integrating vision, touch, and proprioception, to enhance robotic manipulation. VinT-6D comprises 2 million VinT-Sim and 0.1 million VinT-Real splits, collected via simulations in MuJoCo and Blender and a custom-designed real-world platform. This dataset is tailored for robotic hands, offering models with whole-hand tactile perception and high-quality, well-aligned data. To the best of our knowledge, the VinT-Real is the largest considering the collection difficulties in the real-world environment so that it can bridge the gap of simulation to real compared to the previous works. Built upon VinT-6D, we present a benchmark method that shows significant improvements in performance by fusing multi-modal information. The project is available at https://VinT-6D.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00510 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VinT-6D: A Large-Scale Object-in-hand Dataset from Vision, Touch and Proprioception Wan, Zhaoliang Ling, Yonggen Yi, Senlin Qi, Lu Lee, Wangwei Lu, Minglei Yang, Sicheng Teng, Xiao Lu, Peng Yang, Xu Yang, Ming-Hsuan Cheng, Hui Robotics This paper addresses the scarcity of large-scale datasets for accurate object-in-hand pose estimation, which is crucial for robotic in-hand manipulation within the ``Perception-Planning-Control" paradigm. Specifically, we introduce VinT-6D, the first extensive multi-modal dataset integrating vision, touch, and proprioception, to enhance robotic manipulation. VinT-6D comprises 2 million VinT-Sim and 0.1 million VinT-Real splits, collected via simulations in MuJoCo and Blender and a custom-designed real-world platform. This dataset is tailored for robotic hands, offering models with whole-hand tactile perception and high-quality, well-aligned data. To the best of our knowledge, the VinT-Real is the largest considering the collection difficulties in the real-world environment so that it can bridge the gap of simulation to real compared to the previous works. Built upon VinT-6D, we present a benchmark method that shows significant improvements in performance by fusing multi-modal information. The project is available at https://VinT-6D.github.io/. |
| title | VinT-6D: A Large-Scale Object-in-hand Dataset from Vision, Touch and Proprioception |
| topic | Robotics |
| url | https://arxiv.org/abs/2501.00510 |