REA: async brain-cerebellum runtime for robots where policies never wait on the LLM
Pengfei Zhou · hf · 2026-11-02
- Problem: Foundation models reason well but are too slow/costly for fine-grained robot control; fast policies (VLAs) are reactive but generalize poorly. Existing pairings either stall fast execution or miss silent failures.
- Approach: REA is a unified runtime with a deliberative System 2, embodied router System 1, and reactive System 0, under one golden rule: no running policy waits on the brain, with asynchronous coordination.
- Results: High success rates with reduced control overhead across simulated and real robot tasks. From NUS, HPC-AI Lab, InfRec and others.
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