Broadly intelligent agents should form task-specific abstractions that selectively expose the essential elements of a task, while abstracting away the complexity of the raw sensorimotor space. In this work, we present Neuro-Symbolic Predicates, a first-order abstraction language that combines the strengths of symbolic and neural knowledge representations. We outline an online algorithm for inventing such predicates and learning abstract world models. We compare our approach to hierarchical reinforcement learning, vision-language model planning, and symbolic predicate invention approaches, on both in- and out-of-distribution tasks across five simulated robotic domains. Results show that our approach offers better sample complexity, stronger out-of-distribution generalization, and improved interpretability.

翻译：广义智能体应形成任务特定的抽象，这些抽象选择性地暴露任务的基本要素，同时抽象掉原始感知运动空间的复杂性。在本工作中，我们提出神经符号谓词，这是一种结合符号与神经知识表示优势的一阶抽象语言。我们概述了一种用于发明此类谓词并学习抽象世界模型的在线算法。我们将本方法与分层强化学习、视觉语言模型规划以及符号谓词发明方法，在五个模拟机器人领域的分布内与分布外任务上进行了比较。结果表明，我们的方法具有更好的样本效率、更强的分布外泛化能力以及更高的可解释性。