Stanford 论文：不改权重，机器人策略成功率从 9.7% 提升至 43.3%
Stanford 这个思路很聪明：模型权重全冻结，靠编码模型重写外层控制代码，成功率从 9.7% 干到 43.3%，机器人方向值得看看。
Stanford 提出 RoboArchitect，让编码模型分析机器人 rollout 记录，诊断失败原因并重写冻结策略外层的控制代码。该方法在未见场景上的成功率从 9.7% 提升到 43.3%，未见任务上的成功率从 12.7% 升至 40.2%。搜索过程还生成了抬起卡住机械臂等控制例程，以及存储有效抓取深度等数值的持久记忆。在训练数据差异大的基准上，通用策略套上进化后的 harness 后往往胜过微调版本。
Impressive paper from Stanford on harness evolution for robotics.
They improve frozen robot policies from 9.7% to 43.3% success on unseen scenes without changing a single weight.
The gain comes from the harness.
RoboArchitect gives a coding model the robot's rollouts. The coding model diagnoses failures and rewrites the control code around the frozen policy. That code decides when to call the policy, when to use vision tools and when to command the arm directly.
The harness is optimized on one set of episodes and tested on held-out scenes and tasks. Success on unseen tasks rises from 12.7% to 40.2%.
The largest gains appear where the benchmark is far from the policy's training data. Inside an evolved harness, a general-purpose policy often beats the finetuned version of the same policy.
The search also produced control routines such as lifting a jammed arm clear, and it added a persistent memory that stores values like effective grasp depths.
Paper: https://t.co/ZIpfXS81Lb
Chat with Paper: https://t.co/CCgg1eQBvQ
