創発的ニューラルオートマトンポリシー：視覚運動軌跡からの記号構造学習
Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories
視覚運動デモから適応的クラスタリングとL*アルゴリズムの拡張でMealy状態機械を推定し、高レベルプランナーとして利用。離散構造が低レベル残差ネットワークを導き、長期的タスクで高効率・解釈可能なポリシーを実現する。
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著者: Yiyuan Pan, Xusheng Luo, Hanjiang Hu, Peiqi Yu, Changliu Liu
分類: cs.RO
原文アブストラクト
Scaling robot learning to long-horizon tasks remains a formidable challenge. While end-to-end policies often lack the structural priors needed for effective long-term reasoning, traditional neuro-symbolic methods rely heavily on hand-crafted symbolic priors. To address the issue, we introduce ENAP (Emergent Neural Automaton Policy), a framework that allows a bi-level neuro-symbolic policy adaptively emerge from visuomotor demonstrations. Specifically, we first employ adaptive clustering and an extension of the L* algorithm to infer a Mealy state machine from visuomotor data, which serves as an interpretable high-level planner capturing latent task modes. Then, this discrete structure guides a low-level reactive residual network to learn precise continuous control via behavior cloning (BC). By explicitly modeling the task structure with discrete transitions and continuous residuals, ENAP achieves high sample efficiency and interpretability without requiring task-specific labels. Extensive experiments on complex manipulation and long-horizon tasks demonstrate that ENAP outperforms state-of-the-art (SoTA) end-to-end VLA policies by up to 27% in low-data regimes, while offering a structured representation of robotic intent (Fig. 1).
