DOFS: A Real-world 3D Deformable Object Dataset with Full Spatial Information for Dynamics Model Learning

Autor:	Zhang, Zhen, Chu, Xiangyu, Tang, Yunxi, Au, K. W. Samuel
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	This work proposes DOFS, a pilot dataset of 3D deformable objects (DOs) (e.g., elasto-plastic objects) with full spatial information (i.e., top, side, and bottom information) using a novel and low-cost data collection platform with a transparent operating plane. The dataset consists of active manipulation action, multi-view RGB-D images, well-registered point clouds, 3D deformed mesh, and 3D occupancy with semantics, using a pinching strategy with a two-parallel-finger gripper. In addition, we trained a neural network with the down-sampled 3D occupancy and action as input to model the dynamics of an elasto-plastic object. Our dataset and all CADs of the data collection system will be released soon on our website. Comment: 5 pages, 6 figures, 2024 CoRL Workshop on Learning Robot Fine and Dexterous Manipulation: Perception and Control
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.21758 Zobrazit plný text záznamu View this record from Arxiv