One Demo Is All It Takes: Planning Domain Derivation with LLMs from A Single Demonstration

Jinbang Huang, Yixin Xiao, Zhanguang Zhang, Mark Coates, Jianye HAO, Yingxue Zhang

Keywords: Embodied AI, Planning, Task and Motion Planning, LLMs, LLMs for Planning, Domain Model, Planning Domain Inference

TL;DR: Using LLMs and physical simulation to derive planning domains from scratch, thereby enhancing LLM's performance in long-horizon task planning.

Abstract: Pre-trained Large Language Models (LLMs) have shown promise in solving planning problems but often struggle to ensure plan correctness, especially for long-horizon tasks. Meanwhile, traditional robotic task and motion planning (TAMP) frameworks address these challenges more reliably by combining high-level symbolic search with low-level motion planning. However, TAMP relies on the availability of planning domains that typically involve substantial manual effort and domain expertise, limiting its generalizability. We introduce Planning Domain Derivation with LLMs (PDDLLM), a novel approach that combines simulated physical interaction with LLM reasoning to improve planning performance. The method reduces reliance on humans by inferring planning domains from a single annotated task-execution demonstration. Unlike prior domain-inference methods that rely on partially predefined or language descriptions of planning domains, PDDLLM constructs domains entirely from scratch and automatically integrates them with low-level motion planning skills, enabling fully automated long-horizon planning. PDDLLM is evaluated on over 1,200 diverse tasks spanning nine environments and benchmarked against six LLM-based planning baselines, demonstrating superior planning performance, lower token costs, and successful deployment on multiple robot platforms.

Publishedpaper: N/A

Submission Number: 22