Stanford 论文 RoboArchitect：用代码模型自动改写机器人控制代码
Stanford 这篇挺有意思：不微调模型，让代码模型自己看录像改机器人控制代码，成本不到 150 美元，成功率翻了三四倍。
Stanford 的 RoboArchitect 论文让代码模型复盘机器人操作录像，诊断失败原因并自动重写控制代码，最多迭代 5 轮，全程不更新模型权重，单次完整运行平均成本低于 150 美元。在未见过的场景中，成功率从裸策略的 9.7% 提升到 43.3%；在未见过的任务上从 12.7% 提升到 40.2%。在 VLABench 上，包装后的通用策略超过了在同一基准上微调过的同一策略，但所有测试均在仿真环境中完成。
New Stanford paper shows that robot control code no longer needs hand-building for each new robot, because a coding model can diagnose failures and rewrite it automatically.
Robot policies often fail on new setups, and fixing them usually means new demonstrations and finetuning. Wrapping the policy in control code helps, but engineers write it by hand for each robot.
RoboArchitect has a coding model review recorded robot attempts, diagnose failures, and rewrite the control code over up to 5 rounds. A full run cost under $150 on average, with no weights updated.
On unseen scenes, average success rose from 9.7% for the bare policy to 43.3%, and on unseen tasks from 12.7% to 40.2%. On VLABench, the wrapped general policy beat the same policy finetuned on that benchmark, though all tests ran in simulation.
