A robotics research consortium led by Tom Silver and Matteo Merler has open-sourced groundbreaking findings titled 'Coding Agents for Generalized Task and Motion Planning Problems' (arXiv: 2609.30233, GitHub: tomsilver/robocode). Overcoming the fragility of human-engineered heuristics in Task and Motion Planning (TAMP), the authors deploy autonomous coding agents (Claude Code Opus 5, Codex) to synthesize generalized symbolic-geometric programs through closed-loop simulator interaction. Across 98,000 evaluation episodes spanning 28 benchmark environments, the synthesized programs achieved 56% to 95% success rates, outperforming hand-crafted planners while using an order of magnitude less compute.

Key Takeaways

  • ✓Replaces handcrafted robotic heuristics by deploying autonomous coding agents to synthesize generalized TAMP programs.
  • ✓Agents autonomously interact with physics simulators to stress-test edge cases, calibrate dynamics, and refine logic.
  • ✓Evaluated across 28 simulation environments and 98,000 episodes, lifting mean task success from 47% to up to 95%.
  • ✓Maintains superior robustness as object counts scale while requiring an order of magnitude less compute per instance.
  • ✓Full benchmark harness, agent interaction logs, and prompts open-sourced on GitHub under Apache-compatible license.
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Project Links & Resources

Direct Access
Direct access to official project resources and documentation
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Robocode GitHub 官方开源仓库
github.com/tomsilver/robocode
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Agentic GenTAMP 官方项目主页与视频
agenticgentamp.github.io
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ArXiv 论文预印本 (2609.30233)
arxiv.org/abs/2609.30233
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Hugging Face Daily Papers 主页
huggingface.co/papers/2609.30233
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GitHub: tomsilver/robocode%60%EF%BC%9B
github.com/tomsilver/robocode`；
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GitHub: tomsilver/robocode%60
github.com/tomsilver/robocode`
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In-Depth Technical Analysis

Core Background & Industry Pain Points Task and Motion Planning (TAMP) is foundational for autonomous robotics, dictating how manipulators sequence discrete sub-goals while honoring continuous kinematic, collision, and dynamic constraints. Yet generalized TAMP has historically demanded months of handcrafted engineering. Existing symbolic planners suffer from combinatorial explosion when scaling to multi-object cluttered environments, causing catastrophic planning latency or deadlocks. ### Architecture Highlights & Internals The authors demonstrate that autonomous coding agents (Claude Code powered by Opus 5 and Codex) can automate TAMP engineering. Armed with task specifications and physics simulator access within a fixed compute budget, the agent writes, tests, and refines generalized Python programs. It utilizes environment feedback to calibrate physics models, stress-test collision tolerances, and resolve dynamic edge cases. Once finalized, the program is frozen and deployed zero-shot across unseen physical environments. ### Authoritative Benchmarks & Measured Scores Across 980 generated programs evaluated across 98,000 episodes on 28 KinDER and PDDLStream environments: synthesized programs achieved 56% to 95% task success, vastly eclipsing the 47% baseline of hand-crafted planners. Crucially, as obstacle and object counts scaled, the coding agents' programs preserved high success rates while consuming an order of magnitude less runtime compute per episode. ### Developer Hands-on Guide Robotics researchers can clone https://github.com/tomsilver/robocode to run the reproducible evaluation suite, inspect agent reasoning transcripts, and leverage the included prompt templates for custom simulators.