Researchers introduced A2-MEAS, an embodied multi-agent framework that uses code as a scaling interface to give foundation-model-driven robots sustained autonomy through efficient control and failure recovery. A2-MEAS combines learned correction codes with corrective language, spatial evidence and execution feedback, according to the announcement. The framework targets the gap between foundation models guiding robot actions and the reliability robots need to keep operating after failures.
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, execut
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