用单次演示自动构建机器人规划域，提升长程任务成功率
One Demo Is All It Takes: Planning Domain Derivation with LLMs from A Single Demonstration
- 通过大模型+物理仿真从单次演示中推导出动作与谓词
- 在9个环境1200个任务中成功率达基线20%以上
- 无需人工设计规划域，可直接部署到真实机器人
预训练大语言模型在机器人任务规划中展现出潜力，但在长程问题上常难以保证正确性。任务与运动规划（TAMP）通过将符号计划与底层执行结合来解决此问题，但严重依赖人工设计的规划域。为提升长程规划可靠性并减少人工干预，我们提出基于大模型的规划域自动生成框架PDDLLM，该方法结合大模型推理与物理仿真回放，直接从演示轨迹中自动推导符号谓词和动作。与以往依赖部分预设或语言描述的域推断方法不同，PDDLLM无需手动初始化域，并可自动集成至运动规划器生成可执行计划，显著提升长程规划自动化水平。在九个环境中的1200个任务上，PDDLLM优于六种基于大模型的规划基线，成功率至少高出20%，同时降低令牌消耗，并成功部署于多个实体机器人平台。
原文摘要 · Abstract (English)
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.
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