# Coding Agents for Generalized Task and Motion Planning Problems

Source: [arXiv](https://arxiv.org/abs/2609.30233v1)  
Feed7 permalink: https://feed7.dev/p/2609-30233v1-0ik0wbl  
Published: 2026-09-24T17:53:35.000Z  
Trust: Needs Review (needs_review)

## Why Included

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.

## Source Summary

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.

## Practical Implication

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.

## Agent-Ready Context

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.

## Connected Context

Feed7 judgment across 875 accumulated Signals:

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.

- [Efficient LLM-Generated Shuttling Compilers for Complex Trapped-Ion Architectures](https://feed7.dev/p/2607-24714v1-1jmstqw) — Both show coding agents iteratively synthesizing executable systems against domain evaluators; the new study adds frozen-policy testing across held-out task instances and many episodes.
- [Can LLMs Write Fast Multi-GPU Kernels? — Simran Arora, Together AI](https://feed7.dev/p/can-llms-write-fast-multi-gpu-kernels-simran-arora-together-ai-1ii0b8e) — Both find that tool-backed iteration can improve specialized programs, but the kernel results show that this pattern does not eliminate domain-specific performance plateaus.
- [ScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration](https://feed7.dev/p/scarfbench-1u8lniy) — ScarfBench’s low behavioral success contrasts with the stronger simulated planning results, reinforcing that coding-agent capability depends heavily on workload and executable acceptance criteria rather than transferring uniformly.
- [Benchmarking Coding Agents on New vs Legacy Codebases — Denys Linkov, Wisedocs](https://feed7.dev/p/benchmarking-coding-agents-on-new-vs-legacy-codebases-denys-linkov-wised-0vhw2s2) — Freezing programs and evaluating them over 98,000 held-out episodes directly addresses the warning that plausible or quickly produced code is insufficient without end-to-end behavioral verification.

## Context Map

- Layer: benchmark
- Domains: coding
- Topics: coding-agents, agent-evals, tool-use

## Uncertainty

- 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.

## Agent Instruction

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