Learning Object-Centric Spatial Reasoning for Sequential Manipulation in Cluttered Environments
Robotic manipulation in cluttered environments presents a critical challenge for automation. Recent large-scale, end-to-end models demonstrate impressive capabilities but often lack the data efficiency and modularity required for retrieving objects in dense clutter. In this work, we argue for a paradigm of specialized, decoupled systems and present Unveiler, a framework that explicitly separates high-level spatial reasoning from low-level action execution. Unveiler's core is a lightweight, transformer-based Spatial Relationship Encoder (SRE) that sequentially identifies the most critical obstacle for removal. This discrete decision is then passed to a rotation-invariant Action Decoder for...
| Authors | Chrisantus Eze, Ryan C Julian, Christopher Crick |
|---|---|
| Primary category | cs.RO |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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