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This paper introduces Agentic Fast-Slow Planning (AFSP), a hierarchical framework that decouples perception, reasoning, planning, and control for autonomous systems across different timescales. AFSP uses a Vision-Language Model (VLM) for perception to generate symbolic driving directives, which are then converted into executable paths using a Semantic-Guided A* search and an Agentic Refinement Module. Experiments in CARLA demonstrate that AFSP improves robustness, reducing lateral deviation and completion time compared to MPC baselines.
By decoupling high-level reasoning from low-level control, Agentic Fast-Slow Planning enables more robust autonomous navigation, improving lateral deviation by up to 45% and completion time by over 12% compared to traditional MPC methods.
Large foundation models enable powerful reasoning for autonomous systems, but mapping semantic intent to reliable real-time control remains challenging. Existing approaches either (i) let Large Language Models (LLMs) generate trajectories directly - brittle, hard to verify, and latency-prone - or (ii) adjust Model Predictive Control (MPC) objectives online - mixing slow deliberation with fast control and blurring interfaces. We propose Agentic Fast-Slow Planning, a hierarchical framework that decouples perception, reasoning, planning, and control across natural timescales. The framework contains two bridges. Perception2Decision compresses scenes into ego-centric topologies using an on-vehicle Vision-Language Model (VLM) detector, then maps them to symbolic driving directives in the cloud with an LLM decision maker - reducing bandwidth and delay while preserving interpretability. Decision2Trajectory converts directives into executable paths: Semantic-Guided A* embeds language-derived soft costs into classical search to bias solutions toward feasible trajectories, while an Agentic Refinement Module adapts planner hyperparameters using feedback and memory. Finally, MPC tracks the trajectories in real time, with optional cloud-guided references for difficult cases. Experiments in CARLA show that Agentic Fast-Slow Planning improves robustness under perturbations, reducing lateral deviation by up to 45% and completion time by over 12% compared to pure MPC and an A*-guided MPC baseline. Code is available at https://github.com/cjychenjiayi/icra2026_AFSP.
