REA: A Routed Embodied Agent Runtime for Asynchronous Brain-Cerebellum Control
Overview
This research presents REA (Routed Embodied Agent), a unified runtime system that enables asynchronous coordination between deliberative foundation models and fast reactive policies. The system employs a route memory, boundary-triggered judge gates, and shared-budget escalation to prevent slow brain queries from stalling execution. Evaluated on 3,000 test episodes across 60 LIBERO-Pro tasks, REA achieves 95.9% success, outperforming the best single-agent baseline (94.7%) while reducing control steps by 25% and control time by 17%. In a separate study on RoboTwin 2.0, REA reaches 84% success with VLA policies and 86% with VLA+code, compared to 80% for the strongest single VLA baseline (MEDex). The approach also transfers to BEHAVIOR-1K and a real Unitree G1 humanoid robot.
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Reported quantity · test episodes: 3000 other · reported trials
“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”
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Reported quantity · LIBERO-Pro tasks: 60 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalREA: A Routed Embodied Agent Runtime for Asynchronous Brain-Cerebellum Control
This research presents REA (Routed Embodied Agent), a unified runtime system that enables asynchronous coordination between deliberative foundation models and fast reactive policies. The system employs a route memory, boundary-triggered judge gates, and shared-budget escalation to prevent slow brain queries from stalling execution. Evaluated on 3,000 test episodes across 60 LIBERO-Pro tasks, REA achieves 95.9% success, outperforming the best single-agent baseline (94.7%) while reducing control steps by 25% and control time by 17%. In a separate study on RoboTwin 2.0, REA reaches 84% success with VLA policies and 86% with VLA+code, compared to 80% for the strongest single VLA baseline (MEDex). The approach also transfers to BEHAVIOR-1K and a real Unitree G1 humanoid robot.
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