用大模型提取物体级规划模板，提升机器人任务规划效率
Bootstrapping Object-level Planning with Large Language Models
- 从大模型中提取物体级计划模板（FOON），生成可执行的子目标
- 在模拟环境中完成多个抓取放置任务，成功率显著高于传统方法
- 适合需要高效高层规划的机器人系统研发者
我们提出一种新方法，从大型语言模型（LLM）中提取知识，生成描述物体状态高阶变化的物体级计划，并用于启动任务与运动规划（TAMP）。现有方法让LLM直接输出任务计划或PDDL形式的目标，但存在依赖模型完成实际规划、目标难以满足等问题。本方法则将LLM知识以计划模板形式提取为一种称为功能型对象导向网络（FOON）的物体级表示，进而自动生成PDDL子目标。在模拟环境中，该方法在多个抓取放置任务上的表现显著优于其他规划策略。
原文摘要 · Abstract (English)
We introduce a new method that extracts knowledge from a large language model (LLM) to produce object-level plans, which describe high-level changes to object state, and uses them to bootstrap task and motion planning (TAMP). Existing work uses LLMs to directly output task plans or generate goals in representations like PDDL. However, these methods fall short because they rely on the LLM to do the actual planning or output a hard-to-satisfy goal. Our approach instead extracts knowledge from an LLM in the form of plan schemas as an object-level representation called functional object-oriented networks (FOON), from which we automatically generate PDDL subgoals. Our method markedly outperforms alternative planning strategies in completing several pick-and-place tasks in simulation.
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