# OMAR: Open-World Multi-Attribute Reasoning for Scene-Conditioned Shelf Organization

- Source: arXiv cs.RO
- Published: 2026-10-22T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/atmeafi0ybp5d5sdorgyfdul8
- Original: https://arxiv.org/abs/2610.29761

## Summary

An arXiv paper proposes OMAR, an open-world multi-attribute reasoning pipeline for inferring a shelf's latent organization and ranking placement positions for new objects without user preference instructions. A vision-language model first proposes object labels and attributes such as content and packaging type, which are grounded through segmentation, depth, and multi-view association; an LLM then reasons over an instance-level 3D scene graph to produce scene-consistent placement rankings, followed by geometric and kinematic feasibility checks before execution.