发表机构
The Chinese University of Hong Kong, Shenzhen; University of Oxford（香港中文大学（深圳）; 牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究受双系统思维范式启发，提出双过程架构，结合符号求解器与经验驱动模块，在非线性基准环境中提升机器人运动规划的效率、精度与泛化性。
AI 中文摘要
机器人系统已深度融入工业与日常生活，要求兼具速度、精度与可靠性。经典控制与规划方法长期以来能提供强保证，但常以计算效率和适应性为代价。近年，基于学习的方法展现出克服这些局限的潜力，使智能体能利用经验加速决策并解决此前难以处理的问题。本研究从神经符号视角桥接这两种方法，针对非线性运动规划，受《思考，快与慢》范式启发，提出双过程架构，结合鲁棒推理与学习的优势。该框架将最先进的符号求解器作为“系统2”组件，与经验驱动的“系统1”模块相集成；元认知控制器动态协调二者交互，选择依赖快速直觉还是更慢、更精准的推理。通过在多种非线性基准环境中评估该框架，我们证明此架构在规划效率、精度与泛化性上取得持续提升，同时促进任务间的复用。结果表明，将学习与结构化推理紧密耦合，为构建更强大、更具适应性的机器人系统提供了可扩展路径。
英文摘要
Robotic systems are deeply embedded in both industry and everyday life, where they are expected to act with speed, precision, and reliability. Classical control and planning methods have long delivered strong guarantees, but often at the cost of computational efficiency and adaptability. More recently, learning-based approaches have shown promise in overcoming these limitations, enabling agents to leverage experience to accelerate decision-making and address previously intractable problems. In this work, we bridge these two approaches through a neuro-symbolic perspective on nonlinear motion planning. Inspired by the Thinking Fast and Slow paradigm, we introduce a dual-process architecture that combines the strengths of robust reasoning and learning. Our framework integrates state-of-the-art symbolic solvers as a ``System-2'' component with experience-driven ``System-1'' modules. A metacognitive controller dynamically orchestrates their interaction, selecting when to rely on fast intuition versus slower, more precise reasoning. By evaluating the framework across diverse nonlinear benchmark environments, we demonstrate that this architecture yields consistent gains in planning efficiency, accuracy, and generalization, while promoting reuse across tasks. The results suggest that tightly coupling learning with structured reasoning offers a scalable path toward more capable and adaptive robotic systems.
