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

2026-08-06

AI Summary

This work addresses the limitation of existing robotic vision policies that tightly couple scene geometry with specific viewpoints, hindering effective learning from multi-view data and generalization to novel perspectives. To overcome this, the authors propose ARGUS, the first approach to integrate large-scale 3D vision models into robotic observation preprocessing. By aligning images from arbitrary viewpoints into a canonical coordinate frame, ARGUS decouples scene geometry from viewpoint dependency, substantially reducing the learning burden on downstream visuomotor policies. Evaluated on both fixed and highly diverse viewpoint datasets, the method consistently outperforms current state-of-the-art approaches, achieving 4–6× faster training convergence and significantly higher task success rates.

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.

Problem

• visuomotor policies
• scene geometry
• viewpoint diversity
• generalization
• robot manipulation

Innovation

• canonical viewpoint alignment
• large 3D vision models
• visuomotor policy
• viewpoint diversity
• scene geometry disentanglement