Dual Process Motion Planning

The paper introduces a dual‑process architecture for nonlinear motion planning that blends fast, learning‑based intuition (System‑1) with slow, robust symbolic reasoning (System‑2). A metacognitive controller decides when to use each component, aiming to balance speed, precision, and adaptability. Experiments on diverse benchmark environments show consistent improvements in planning efficiency, accuracy, and generalization, highlighting the benefits of integrating learning with structured reasoning.

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• agents
• robotics
• benchmarks