ARGUS: Aligning Robot Scene Geometry Under Shifting Views with Large 3D Vision Models

Engineering notes

Engineering notes will be added by the Robot Papers editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

Large-scale visuomotor policies have demonstrated impressive performance across a wide range of robot manipulation tasks. However, despite this success, manipulation polices often entangle scene geometry with the corresponding viewpoint, learning where objects lie in an image rather than where it lies in the task space. This entanglement inherently limits the corresponding policy's ability to learn from viewpoint-diverse datasets (ex. DROID, BridgeV2) and generalize beyond the viewpoints captured in their training data. In this work, we present ARGUS, an observation pre-processing pipeline that uses large-scale 3D vision models to align image observations from arbitrary camera viewpoints into a canonical viewpoint before passing it to downstream visuomotor policies. Experiments across training datasets with varying levels of viewpoint diversity, from fixed multi-view camera configurations to highly varied camera placements, show that our method consistently outperforms prior approaches across both limited-view and view-diverse training regimes. In efficiency comparisons, ARGUS demonstrates an ability to learn from view-diverse data, converging to high success rates 4-6x faster than previous methods by leveraging a simplified observation space. Overall, our findings show that leveraging large-scale 3D vision models reduces the learning burden on visuomotor policies, enabling more efficient learning from large-scale, viewpoint-diverse robot datasets.

Links and sources

• Official / arXiv page
• PDF from original source

Comments