Building robots that can automate labor-intensive tasks has long been the core motivation behind the advancements in computer vision and the robotics community. Recent interest in leveraging 3D algorithms, particularly neural fields, has led to advancements in robot perception and physical understanding in manipulation scenarios. However, the real world's complexity poses significant challenges. To tackle these challenges, we present Robo360, a dataset that features robotic manipulation with a dense view coverage, which enables high-quality 3D neural representation learning, and a diverse set of objects with various physical and optical properties and facilitates research in various object manipulation and physical world modeling tasks. We confirm the effectiveness of our dataset using existing dynamic NeRF and evaluate its potential in learning multi-view policies. We hope that Robo360 can open new research directions yet to be explored at the intersection of understanding the physical world in 3D and robot control.

Robo360是一个数据集，具有密集的视角覆盖，可实现高质量的3D神经表示学习，包含多样的物体和各种物理光学属性，促进各种物体操作和物理世界建模任务的研究。通过使用现有的动态NeRF验证我们数据集的有效性，并评估其在学习多视角策略方面的潜力。我们希望Robo360能在理解3D物理世界和机器人控制交叉领域开辟新的研究方向。

Robo360：一种三维全方位多材料机器人操作数据集