大規模モデルの推論とリアルタイム制御を橋渡しするエージェント型高速・低速計画
Bridging Large-Model Reasoning and Real-Time Control via Agentic Fast-Slow Planning
大規模言語モデルの推論と実時間制御を階層的に統合する枠組みを提案し、CARLA実験でMPC単独やA*誘導MPCと比べて外乱下での横偏差を最大45%削減、完了時間を12%以上短縮した。
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著者: Jiayi Chen, Shuai Wang, Guangxu Zhu, Chengzhong Xu
分類: cs.RO, cs.AI
原文アブストラクト
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.
