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。