Pre-trained large language models (LLMs) show promise for robotic task planning but often struggle to guarantee correctness in long-horizon problems. Task and motion planning (TAMP) addresses this by grounding symbolic plans in low-level execution, yet it relies heavily on manually engineered planning domains. To improve long-horizon planning reliability and reduce human intervention, we present Planning Domain Derivation with LLMs (PDDLLM), a framework that automatically induces symbolic predicates and actions directly from demonstration trajectories by combining LLM reasoning with physical simulation roll-outs. Unlike prior domain-inference methods that rely on partially predefined or language descriptions of planning domains, PDDLLM constructs domains without manual domain initialization and automatically integrates them with motion planners to produce executable plans, enhancing long-horizon planning automation. Across 1,200 tasks in nine environments, PDDLLM outperforms six LLM-based planning baselines, achieving at least 20\% higher success rates, reduced token costs, and successful deployment on multiple physical robot platforms.

翻译：预训练大型语言模型（LLM）在机器人任务规划中展现出潜力，但在长时程问题中往往难以保证正确性。任务与运动规划（TAMP）通过将符号规划与底层执行相结合来解决此问题，但其高度依赖人工设计的规划领域。为提高长时程规划的可靠性并减少人工干预，我们提出了基于LLM的规划领域推导框架（PDDLLM），该框架通过结合LLM推理与物理仿真推演，直接从演示轨迹中推导符号谓词与动作。与先前依赖部分预定义或语言描述规划领域的领域推断方法不同，PDDLLM无需人工领域初始化即可构建规划领域，并自动将其与运动规划器集成以生成可执行计划，从而提升了长时程规划的自动化水平。在九个环境共1,200项任务的测试中，PDDLLM优于六种基于LLM的规划基线方法，实现了至少20%的成功率提升，降低了令牌消耗成本，并在多个实体机器人平台上成功部署。