Article

ENAP: Neural Automaton Policies That Learn Symbolic Structure from Visuomotor Trajectories

The authors propose ENAP (Emergent Neural Automaton Policy), a neuro-symbolic imitation learning framework that uses adaptive clustering and an extension of the L* algorithm to learn a Mealy state machine from demonstrations as an interpretable high-level planner, which then guides a low-level reactive residual network to learn continuous control via behavior cloning.

Part of the event: Authors propose ENAP neuro-symbolic imitation learning framework

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