Coding Agents for Generalized Task and Motion Planning Problems
Coding agents synthesized reusable robot-planning programs that beat available hand-built planners on held-out instances, suggesting interactive code generation can replace substantial domain engineering.
Claude Code and Codex synthesized programs in **28 simulated environments** under fixed budgets. Researchers froze each program, then ran **98,000 episodes** on held-out instances.
For agent builders, the useful pattern is simulator-backed synthesis: let the agent probe dynamics, test edge cases, and refine code before freezing it for deployment. This can produce reusable policies rather than solving each instance afresh.
Claude Code and Codex synthesized programs in **28 simulated environments** under fixed budgets. Researchers froze each program, then ran **98,000 episodes** on held-out instances. For agent builders, the useful pattern is simulator-backed synthesis: let the agent probe dynamics, test edge cases, and refine code before freezing it for deployment. This can produce reusable policies rather than solving each instance afresh. On the 16 environments with a planner, agents averaged **56%–95% success versus 47%** and used about an order of magnitude less computation per instance. These are simulation results, not evidence of equivalent performance on physical robots.
This supplies unusually large held-out evidence for simulator-backed program synthesis: agents can probe dynamics during development, freeze reusable policies, and then outperform a per-instance planner on some simulated task-and-motion workloads with lower inference-time computation. It strengthens iterative coding-agent evaluation while sharply limiting the conclusion to the tested simulations rather than physical deployment.