Abductive Learning for Neuro-Symbolic Grounded Imitation

Recent learning-to-imitation methods have shown promise in planning by imitating within the observation-action space, yet they remain constrained in open environments, especially for long-horizon tasks. In contrast, while traditional symbolic planning excels in such tasks through logical reasoning over human-defined symbolic spaces, it struggles with high-dimensional visual inputs encountered in real-world scenarios. In this work, we draw inspiration from abductive learning and introduce a novel framework ABductive Imitation Learning (ABIL) that integrates the benefits of data-driven learning and symbolic-based reasoning, enabling long-horizon planning. Specifically, we employ abductive reasoning to understand the demonstrations in symbolic space and design the principles of sequential consistency to resolve the conflicts between perception and reasoning. ABIL generates predicate candidates to facilitate the perception from raw observations to symbolic space without laborious predicate annotations, providing a groundwork for symbolic planning. With the symbolic understanding, we develop a policy ensemble with base policies designed around different logical objectives, managed through symbolic reasoning. Experiments demonstrate that our method successfully comprehends observations with task-relevant symbolics to aid imitation learning. Importantly, ABIL demonstrates improved data efficiency and generalization across various long-horizon tasks, highlighting it as a promising solution for long-horizon planning. Project website: https://www.lamda.nju.edu.cn/shaojj/KDD25_ABIL/.