用大模型预判任务，规划多任务并行，效率提升31%。
Anticipate & Act : Integrating LLMs and Classical Planning for Efficient Task Execution in Household Environments
- 用少量提示词调用大模型预判后续任务，生成高层目标
- 将预判任务作为目标输入经典规划器，生成精细动作序列
- 在虚拟家庭环境验证，比单任务执行快31%
在家庭环境中执行如整理床铺或做早餐等辅助任务时，现有系统通常逐个完成任务，效率较低。本文提出一种融合大语言模型与经典规划的方法：通过少量提示词利用大模型的通用知识进行高层任务预判，并将预判结果作为目标输入经典规划系统，生成能同时达成多个目标的细粒度动作序列。我们在VirtualHome环境中对框架进行了真实场景下的落地与评估，结果显示，相比不考虑未来任务的系统，本方法执行时间减少31%。
原文摘要 · Abstract (English)
Assistive agents performing household tasks such as making the bed or cooking breakfast often compute and execute actions that accomplish one task at a time. However, efficiency can be improved by anticipating upcoming tasks and computing an action sequence that jointly achieves these tasks. State-of-the-art methods for task anticipation use data-driven deep networks and Large Language Models (LLMs), but they do so at the level of high-level tasks and/or require many training examples. Our framework leverages the generic knowledge of LLMs through a small number of prompts to perform high-level task anticipation, using the anticipated tasks as goals in a classical planning system to compute a sequence of finer-granularity actions that jointly achieve these goals. We ground and evaluate our framework's abilities in realistic scenarios in the VirtualHome environment and demonstrate a 31% reduction in execution time compared with a system that does not consider upcoming tasks.
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