GAPL: Grounded Action-effect Policy Learning for LLM-Based Trajectory Planning
Bridging LLM reasoning with simulation-based grounding to solve precision issues in autonomous trajectory planning.
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 dyna...
ArXiv CS.RO
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GAPL bridges the gap between high-level LLM reasoning and low-level physical control by grounding abstract action plans in real-world simulation dynamics. This hybrid approach significantly reduces hallucination-driven errors, providing a safer and more reliable framework for autonomous vehicle navigation.
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