GAPL: LLMベース軌道計画のための接地された行動効果ポリシー学習
GAPL: Grounded Action-effect Policy Learning for LLM-Based Trajectory Planning
自動運転の軌道計画において、LLMの推論能力とシミュレーションによる環境ダイナミクスの接地を組み合わせ、PPOでポリシーを最適化する閉ループフレームワークGAPLを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Zhihong Cui, Hengyu Liu, Zhangkai Wu, Yushuai Li, Tianyi Li, Peiyuan Guan, Amir Taherkordi, Tor Skeie
分類: cs.RO
原文アブストラクト
Trajectory planning for autonomous driving requires both high-level reasoning and precise low-level control. Large Language Models (LLMs) offer semantic-rich planning capabilities, however, their application is limited by hallucinated reasoning, poor grounding in environment dynamics, and limited numerical precision in control. We propose GAPL (Grounded Action-effect Policy Learning), a unified framework that integrates LLM-based effect estimation, simulation-based effect grounding, and policy optimization into a closed-loop system. GAPL consists of three modules: (1) an LLM-based Effect Evaluator for structured multi-dimensional action-effect estimation; (2) a Simulation-based Effect Grounder that predicts dynamics-consistent effects from simulator rollouts; and (3) an Effect-Aware Decision Maker that grounds LLM effect estimates against simulation via a distiller to guide Proximal Policy Optimization (PPO)-based policy learning. Experiments on four Highway-env scenarios demonstrate that GAPL consistently outperforms baselines, achieving average reductions of {0.76, 0.86, 2.00} in collision rate, average displacement error (ADE), and final displacement error (FDE), and an average reward gain of 1.44.
