Foundation Models

Chain-of-Thought Planning

Chain-of-thought planning is the use of intermediate reasoning steps, generated in natural language or structured text by a large language or vision-language model, to decompose a robot task before committing to actions. Rather than mapping observations directly to motor commands, the model reasons about subgoals, object relations, and preconditions, as explored in embodied reasoning systems and VLAs with reasoning traces. This improves long-horizon coherence and interpretability of robot behavior.

Why it matters for physical AI

Explicit reasoning traces let robot foundation models handle multi-step tasks, expose auditable intermediate decisions for safety review, and transfer web-scale reasoning ability into embodied decision-making.