Anticipate & Act : Integrating LLMs and Classical Planning for Efficient Task Execution in Household Environments
This work addresses efficiency improvements for assistive agents in household tasks, representing an incremental advance by combining existing methods.
The paper tackles the problem of inefficient task execution in household environments by integrating LLMs for task anticipation with classical planning for action sequencing, resulting in a 31% reduction in execution time compared to non-anticipatory systems.
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.