  Large language models (LLMs) trained on code completion have been shown to be
capable of synthesizing simple Python programs from docstrings [1]. We find
that these code-writing LLMs can be re-purposed to write robot policy code,
given natural language commands. Specifically, policy code can express
functions or feedback loops that process perception outputs (e.g.,from object
detectors [2], [3]) and parameterize control primitive APIs. When provided as
input several example language commands (formatted as comments) followed by
corresponding policy code (via few-shot prompting), LLMs can take in new
commands and autonomously re-compose API calls to generate new policy code
respectively. By chaining classic logic structures and referencing third-party
libraries (e.g., NumPy, Shapely) to perform arithmetic, LLMs used in this way
can write robot policies that (i) exhibit spatial-geometric reasoning, (ii)
generalize to new instructions, and (iii) prescribe precise values (e.g.,
velocities) to ambiguous descriptions ("faster") depending on context (i.e.,
behavioral commonsense). This paper presents code as policies: a robot-centric
formulation of language model generated programs (LMPs) that can represent
reactive policies (e.g., impedance controllers), as well as waypoint-based
policies (vision-based pick and place, trajectory-based control), demonstrated
across multiple real robot platforms. Central to our approach is prompting
hierarchical code-gen (recursively defining undefined functions), which can
write more complex code and also improves state-of-the-art to solve 39.8% of
problems on the HumanEval [1] benchmark. Code and videos are available at
https://code-as-policies.github.io
