让AI自己写规划程序，比人类手写的还管用
机器人做“先想后动”的规划一直很难：既要选离散动作，又要满足几何、力学这些连续约束，两者死死绑在一起。过去要靠专家写大量专用代码。这篇让编程智能体自己动手：给它任务描述和模拟器，它边试边写程序，写完后冻结，直接拿去跑没见过的场景。结果三个智能体配置在28个模拟环境上，平均成功率56%到95%，而人类手写的规划器只有47%；物体数量越多，优势越大，计算量还少一个数量级。日志显示智能体会用交互来校准物理模型、测边界情况、改策略。它不是你明天就能用的东西，但提示了方向：与其继续给机器人写专用规划器，不如让AI自己写。
📄 原文摘要(英文)
Task and motion planning (TAMP) problems remain difficult even with full observability and object-centric states because discrete decisions are tightly coupled to geometric, kinematic, and dynamic constraints. Generalized TAMP addresses this difficulty by exploiting regularities across problem instances to reduce planning effort on new instances. However, existing methods require substantial TAMP-specific engineering. We investigate whether coding agents can automate this process by synthesizing programs that generalize across instances. Given a task description and simulator access, each agent chooses how to interact with the environment while developing a program within a fixed synthesis budget. The program is then frozen and evaluated on unseen instances. We evaluate Claude Code (Opus 5) and Codex (GPT-5.6 Sol and GPT-6 Astra) on 28 simulated environments from KinDER and PDDLStream, with object counts beyond those evaluated in the original benchmark. Across all program synthesis methods, we evaluate 980 generated programs on 100 held-out instances each, 98,000 evaluation episodes in total. Overall, we find that coding agents are surprisingly effective at generalized TAMP: all three agent configurations outperform hand-engineered planners, one-shot generation, and an LLM-based generalized planning baseline in mean success (56% to 95% versus 47% for the planners, on the 16 environments where a planner is available). As object counts grow, the agents' programs maintain higher success than the planner, using an order of magnitude less computation per instance on average. Logs show agents using interaction to calibrate physical models, test edge cases, and refine strategies. We release all code, including the full prompts given to the agents. These findings suggest that coding agents are a strong baseline for generalized TAMP.