Coding agents can automatically synthesize generalizable planning programs better than traditional planners or one-shot LLM generation, suggesting LLMs writing code is a practical approach to automating robotics planning.
This paper shows that AI coding agents (like Claude and GPT models) can automatically write programs that solve robot task-and-motion planning problems across different scenarios. Given access to a simulator, agents develop reusable code within a budget, then freeze it for evaluation on new instances.