RoboPIN: Grounded Embodied Reasoning via Pinned Chain-of-Thought

• URL: http://arxiv.org/abs/2606.15753v1
• Date: Sun, 14 Jun 2026 11:30:42 GMT
• Title: RoboPIN: Grounded Embodied Reasoning via Pinned Chain-of-Thought
• Abstract summary: Embodied reasoning requires models to perceive task-relevant objects and spaces in physical environments.<n>Current vision-language models rely on text-only or coordinate-augmented chain-of-thought.<n>We propose Pinned Chain-of-Thought, a structured reasoning paradigm that pins every reasoning step to visual evidence.
• Score: 50.36951914894845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Embodied reasoning requires models to perceive task-relevant objects and spaces in physical environments and maintain consistent visual grounding throughout multi-step reasoning. However, current vision-language models rely on text-only or coordinate-augmented chain-of-thought, where entity references remain implicit and ambiguous. This may cause the reasoning process to decouple from visual evidence, entity references to drift across steps, and a causal disconnection between the reasoning trajectory and the final answer, with these problems further amplified in multi-view scenarios due to cross-view appearance changes. To address these issues, we propose Pinned Chain-of-Thought (\pincot{}), a structured reasoning paradigm that pins every reasoning step to visual evidence. \pincot{} introduces the concept of \reasoninganchor{}, which binds each task-relevant entity to a structured visual anchor with entity name, unique identity, view index, and spatial grounding, enabling consistent entity tracking across reasoning steps and views. We build a fully automated data generation pipeline to construct \dataset{}, a high-quality \pincot{}-formatted reasoning dataset. We then train \method{} through three-stage post-training that progressively injects embodied knowledge, structured reasoning ability, and process-supervised alignment, with rewards that directly constrain both anchor localization and identity consistency during reasoning. On 14 benchmarks covering embodied spatial reasoning, multi-view reasoning, and pointing, \method{} with only 4B parameters consistently outperforms 7B level open-source embodied models, achieving a 12\% average improvement over the strongest 7B baseline, Mimo-Embodied. Further analysis shows that \pincot{} improves grounding accuracy and cross-step identity consistency, validating the effectiveness of process supervision.

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