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arXiv · 2502.02066

Anticipate & Act : Integrating LLMs and Classical Planning for Efficient Task Execution in Household Environments

Raghav Arora·Shivam Singh·Karthik Swaminathan·Ahana Datta·Snehasis Banerjee·Brojeshwar Bhowmick·Krishna Murthy Jatavallabhula·Mohan Sridharan·Madhava Krishna
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Abstract

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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Raghav Arora, Shivam Singh, Karthik Swaminathan, Ahana Datta, Snehasis Banerjee, Brojeshwar Bhowmick, Krishna Murthy Jatavallabhula, Mohan Sridharan, Madhava Krishna. 2025-02-04. Anticipate & Act : Integrating LLMs and Classical Planning for Efficient Task Execution in Household Environments. https://arxiv.org/abs/2502.02066

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