[2603.02511] Learning Object-Centric Spatial Reasoning for Sequential Manipulation in Cluttered Environments

Computer Science > Robotics arXiv:2603.02511 (cs) [Submitted on 3 Mar 2026] Title:Learning Object-Centric Spatial Reasoning for Sequential Manipulation in Cluttered Environments Authors:Chrisantus Eze, Ryan C Julian, Christopher Crick View a PDF of the paper titled Learning Object-Centric Spatial Reasoning for Sequential Manipulation in Cluttered Environments, by Chrisantus Eze and 2 other authors View PDF HTML (experimental) Abstract: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 execution. We demonstrate that this decoupled architecture is not only more computationally efficient in terms of parameter count and inference time, but also significantly outperforms both classic end-to-end policies and modern, large-model-based baselines in retrieving targets from dense clutter. The SRE is trained in two stages: imitation learning from he...