机器人不再等大脑想完再动
机器人一直有个尴尬：负责想的大模型又慢又贵，负责动的快策略又笨不会变通。以往要么让机器人每一步都等大脑，要么让快策略出错才找大脑，结果不是卡死就是漏掉故障。这篇把两者装进一个运行时，立了条铁规矩：正在执行的动作永远不等大脑。它提前把每个策略的擅长领域记下来，只在边界情况才去问大脑，还设了共享预算防止无限重试。在3000轮测试里成功率95.9%，比最好的单一策略高一点，但控制步数和时间各省了25%和17%，还跑通了真的人形机器人。它不是你明天能用上的东西，但这条「动作不等思考」的架构原则，可能是机器人从演示玩具走向干活的关键一步。
📄 原文摘要(英文)
Foundation models are becoming remarkably reliable at high-level reasoning, yet they remain too slow and costly to serve as fine-grained controllers. Conversely, fast policies like VLAs and whole-body controllers offer more efficient reflexes, but struggle to generalize. While pairing a deliberative brain (foundation models) with a reactive cerebellum (fast policies) seems natural for embodied systems, two obstacles remain: methods are built and scored on incompatible stacks, and their asynchronous coordination is difficult. For example, an agent that calls the VLA in the agentic loop can stall the fast execution, while a VLA that calls the agent on errors tends to re-trigger fallbacks repeatedly or completely miss silent failures. To address this, we propose REA (Routed Embodied Agent), a unified runtime where a deliberative System 2, an embodied router System 1 and reactive System 0 policies cooperate under a single golden rule - no running policy waits on the brain. Instead of querying the brain at each failure, REA employs a route memory that stores each policy's measured success ahead of time, a boundary-triggered judge gate that consults the brain on policy execution, and a shared-budget escalation ladder for bounded recovery. Evaluated on 3,000 test episodes across 60 LIBERO-Pro tasks, REA reaches 95.9% success and beats the best single-agent baseline in its own policy pool (94.7%) while reducing control steps by 25% and control time by 17%. In a separate 100-episode RoboTwin 2.0 study, REA achieves 84% success with VLA policies and 86% with VLA+code, compared with 80% for the strongest single VLA baseline MEDex. These results demonstrate learned and programmatic policies operating within the same runtime. REA also transfers to BEHAVIOR-1K and a real Unitree G1 humanoid, achieving the highest observed success among the compared methods.