用神经符号谓词构建机器人规划的抽象世界模型，提升泛化与可解释性。
VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning
- 融合符号逻辑与神经网络，在线生成可解释的抽象谓词
- 在5个仿真机器人任务中样本效率更高，跨分布泛化更强
- 适合需要可靠、透明决策的机器人系统研发
通用智能体应能形成任务相关的抽象，仅保留任务关键要素，忽略原始感知运动空间的复杂性。本文提出神经符号谓词，一种一阶抽象语言，结合符号与神经知识表示的优势。我们设计了一种在线算法，用于发明此类谓词并学习抽象世界模型。在五个模拟机器人领域上，对比了分层强化学习、视觉-语言模型规划及符号谓词生成方法，在分布内和分布外任务中均表现更优。结果表明，该方法具有更低的样本复杂度、更强的分布外泛化能力，并显著提升可解释性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。