ニューロシンボリック模倣学習：スキル学習のための記号的抽象化の発見
Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning
模倣学習において、低レベルな状態行動空間を記号的に抽象化し、タスクをサブタスクに分解して記号プランニングとニューラルスキルを組み合わせる枠組みを提案。データ効率・汎化・解釈性が向上することを示した。
著者: Leon Keller, Daniel Tanneberg, Jan Peters
分類: cs.AI, cs.LG, cs.RO
原文アブストラクト
Imitation learning is a popular method for teaching robots new behaviors. However, most existing methods focus on teaching short, isolated skills rather than long, multi-step tasks. To bridge this gap, imitation learning algorithms must not only learn individual skills but also an abstract understanding of how to sequence these skills to perform extended tasks effectively. This paper addresses this challenge by proposing a neuro-symbolic imitation learning framework. Using task demonstrations, the system first learns a symbolic representation that abstracts the low-level state-action space. The learned representation decomposes a task into easier subtasks and allows the system to leverage symbolic planning to generate abstract plans. Subsequently, the system utilizes this task decomposition to learn a set of neural skills capable of refining abstract plans into actionable robot commands. Experimental results in three simulated robotic environments demonstrate that, compared to baselines, our neuro-symbolic approach increases data efficiency, improves generalization capabilities, and facilitates interpretability.
