REA: A Routed Embodied Agent Runtime for Asynchronous Brain-Cerebellum Control
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabREA (Routed Embodied Agent) is a runtime that coordinates a slow deliberative foundation-model 'brain' (System 2), an embodied router (System 1), and fast reactive robot policies (System 0) under the rule that no running policy waits on the brain. It uses a route memory storing each policy's measured success in advance, a boundary-triggered judge gate that consults the brain only at policy execution boundaries, and a shared-budget escalation ladder for bounded recovery. The paper reports 95.9% success over 3,000 LIBERO-Pro episodes, versus 94.7% for the best single-agent baseline, with 25% fewer control steps and 17% less control time. It also reports results on RoboTwin 2.0, BEHAVIOR-1K, and a Unitree G1 humanoid.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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REA (Routed Embodied Agent) is a runtime that coordinates a slow deliberative foundation-model 'brain' (System 2), an embodied router (System 1), and fast reactive robot policies (System 0) under the rule that no running policy waits on the brain. It uses a route memory storing each policy's measured success in advance, a boundary-triggered judge gate that consults the brain only at policy execution boundaries, and a shared-budget escalation ladder for bounded recovery. The paper reports 95.9% success over 3,000 LIBERO-Pro episodes, versus 94.7% for the best single-agent baseline, with 25% fewer control steps and 17% less control time. It also reports results on RoboTwin 2.0, BEHAVIOR-1K, and a Unitree G1 humanoid.
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Key findings from the stored brief
- REA's golden rule is that no running policy waits on the brain, keeping ongoing execution independent of the brain's latency.
- Route memory stores each policy's measured success ahead of time, replacing querying the brain at every failure.
- On 3,000 LIBERO-Pro episodes, REA reaches 95.9% success versus 94.7% for the best single-agent baseline in its policy pool.
- REA reduces control steps by 25% and control time by 17% relative to the compared baseline, per the paper's reported results.
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Why this may matter
Pairing slow foundation-model reasoning with fast robot policies is a common goal, but asynchronous coordination often stalls execution or misses failures. REA offers one concrete design for handling that coordination in a unified runtime.
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