TRACER: Texture-Robust Affordance Chain-of-Thought for Deformable-Object Refinement

The central challenge in robotic manipulation of deformable objects lies in aligning high-level semantic instructions with physical interaction points under complex appearance and texture variations. Existing vision-based affordance prediction methods often suffer from boundary overflow and fragmented functional regions. To address these issues, we propose TRACER, a Texture-Robust Affordance Chain-of-Thought for Deformable-Object Region Grounding framework that maps hierarchical semantic reasoning to appearance-robust and physically consistent interaction regions. Specifically, a Tree-structured Affordance Chain-of-Thought (TA-CoT) decomposes high-level task intentions into hierarchical affordance-semantic instructions. A Spatially-Constrained Boundary Refinement (SCBR) mechanism suppresses prediction spillover and guides responses toward valid object regions. Furthermore, an Interactive Convergent Refinement Flow (ICRF) refines dispersed affordance responses into coherent regions, improving spatial continuity and physical plausibility. Experiments on the Fine-AGDDO15 dataset and a real-world robotic platform demonstrate that TRACER improves affordance grounding precision across diverse textures and patterns. It also enhances long-horizon manipulation success, bridging high-level semantic reasoning and low-level physical execution. The source code and dataset will be made publicly available at https://github.com/Dikay1/TRACER.