ENAP extracts an emergent Mealy automaton from visuomotor trajectories to act as a high-level planner for a low-level residual policy, yielding up to 27% higher success than end-to-end VLA policies in low-data regimes.
Learning regular sets from queries and counterexamples.Information and computation, 75(2): 87–106
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Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories
ENAP extracts an emergent Mealy automaton from visuomotor trajectories to act as a high-level planner for a low-level residual policy, yielding up to 27% higher success than end-to-end VLA policies in low-data regimes.