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

- Source: arXiv cs.RO
- Published: 2026-10-26T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/y7kbw31xq4w9w2jw6kz9rssy9
- Original: https://arxiv.org/abs/2603.25903

## Summary

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.