Foundation Models

Code as Policies

Code as Policies is an approach from Liang et al. (Google, 2022) in which a large language model writes executable policy code, composing perception APIs, control primitives, and loops, directly from natural language instructions. Generated programs can invoke third-party libraries, define reusable functions, and express spatial reasoning numerically, enabling zero-shot generalization to novel instructions without policy training. It demonstrated that LLM code generation can serve as a robot planning interface.

Why it matters for physical AI

Program synthesis gives language models a precise, verifiable interface to robot skills, complementing end-to-end VLAs with interpretable, editable behavior that engineers can inspect before execution.