Foundation Models

Tool-Augmented Policy

A tool-augmented policy is a robot decision-making system, typically built around a large language or vision-language model, that can invoke external software tools during operation, such as perception modules, motion planners, calculators, databases, or code execution, rather than producing all outputs from its own weights. Systems in the Code-as-Policies lineage exemplify the pattern, with the model composing calls to perception APIs and control primitives to solve tasks it was never explicitly trained on.

Why it matters for physical AI

Delegating geometry, arithmetic, and safety-critical checks to verified tools compensates for known weaknesses of end-to-end models, and tool-calling architectures offer a pragmatic path to reliable long-horizon autonomy with auditable intermediate steps.