Paper ID: 2501.00510

VinT-6D: A Large-Scale Object-in-hand Dataset from Vision, Touch and Proprioception

Zhaoliang Wan, Yonggen Ling, Senlin Yi, Lu Qi, Wangwei Lee, Minglei Lu, Sicheng Yang, Xiao Teng, Peng Lu, Xu Yang, Ming-Hsuan Yang, Hui Cheng

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 this https URL

Submitted: Dec 31, 2024