Guide, Think, Act: Interactive Embodied Reasoning for Vision-Language-Action Model

In this paper, we propose GTA-VLA (Guide, Think,Act), an interactive Vision-Language-Action (VLA) framework thatenables spatially steerable embodied reasoning by allowing users to guiderobot policies with explicit visual cues. Existing VLA models learn adirect "Sense-to-Act" mapping from multimodal observations to robotactions. While effective within the training distribution, such tightly cou-pled policies are brittle under out-of-domain (OOD) shifts and difficultto correct when failures occur. Although recent embodied Chain-of-Thought (CoT) approaches expose intermediate reasoning, they stilllack a mechanism for incorporating human spatial guidance, limitingtheir ability to resolve visual ambiguities or recover from mistakes. Toaddress this gap, our framework allows users to optionally guide thepolicy with spatial priors, such as affordance points, boxes, and traces,which the subsequent reasoning process can directly condition on. Basedon these inputs, the model generates a unified spatial-visual Chain-of-Thought that integrates external guidance with internal task planning,aligning human visual intent with autonomous decision-making. Forpractical deployment, we further couple the reasoning module with alightweight reactive action head for efficient action execution. Extensiveexperiments demonstrate the effectiveness of our approach. On thein-domain SimplerEnv WidowX benchmark, our framework achievesa state-of-the-art 81.2% success rate. Under OOD visual shifts andspatial ambiguities, a single visual interaction substantially improvestask success over existing methods, highlighting the value of interactivereasoning for failure recovery in embodied control.