机器人失败后自己爬起来
机器人做砸了之后能自己纠正，比一次做对更值钱。这套系统把「纠错」本身变成一种可学习的代码：把自然语言的纠正、空间证据、执行历史和动作建议打包成一段「纠错码」，训练时把它吸收进策略权重，部署时权重不动、只换新码就能改行为。最硬的一个数字在倒滤纸冲咖啡：两个处理滤纸的步骤失败后，靠带历史条件的适配器把完成率从 14% 拉到 94%、从 8% 拉到 82%，对照组只是让原模型重试。在 50 个任务的公开子集上得分 0.5755，是 2025 年冠军报告值的 2.21 倍(不同评测版本，别当同一把尺);100 个任务上 0.6411、成功率 45.40%;12 个真实家庭场景任务平均成功率 57.92%，对比 pi0.5 的 26.67%。它不是你明天能买到的家用机器人，但「失败后不僵住、能接着干」这件事，正从演示走向可复现的工程。
📄 原文摘要(英文)
Foundation models increasingly guide robot actions, yet sustained autonomy requires efficient control and failure recovery. We introduce A2-MEAS, an embodied multi-agent framework built around code as a scaling interface. Learned correction codes combine corrective language, spatial evidence, execution history, and action proposals. MetaBrain provides training supervision and runtime guidance. A correction encoder and adapter absorb this knowledge into policy weights during training, while fresh codes adapt behavior with frozen weights at deployment. The action model, A2-WLA, adds motion-aware history and a latent world model to a vision-language-action policy. Surprisingly, this simple design with action pretraining and task specialization reaches 0.5755 Q-score on the 50-task BEHAVIOR 2026 public subset before online correction, 2.21x the 2025 winner's reported 0.2605 across different benchmark editions and training configurations. On all 100 BEHAVIOR 2026 public tasks, A2-MEAS achieves 0.6411 Q-score and 45.40% task success. On RoboTwin 2.0, A2-WLA achieves 93.98% average success, rising to 95.05% with the complete system. Across 12 real-robot tasks in five household settings, A2-WLA achieves 57.92% average success versus 26.67% for pi0.5. In long-horizon pour-over coffee preparation, a history-conditioned adapter raises post-failure completion at two filter-handling steps from 14% to 94% and from 8% to 82%, compared with A2-WLA retries. Our training infrastructure delivers 427.1 samples per second, 4.03x the throughput of a pi0.5 reference implementation on matched hardware. We will release the system, models, training scripts, and infrastructure to support efficient embodied learning.