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

• URL: http://arxiv.org/abs/2502.02066v1
• Date: Tue, 04 Feb 2025 07:31:55 GMT
• Title: Anticipate & Act : Integrating LLMs and Classical Planning for Efficient Task Execution in Household Environments
• Authors: Raghav Arora, Shivam Singh, Karthik Swaminathan, Ahana Datta, Snehasis Banerjee, Brojeshwar Bhowmick, Krishna Murthy Jatavallabhula, Mohan Sridharan, Madhava Krishna,
• Abstract summary: We develop a framework for anticipating household tasks and computing an action sequence that jointly achieves these tasks. We demonstrate a 31% reduction in execution time compared with a system that does not consider upcoming tasks.
• Score: 16.482992646001996
• License:
• 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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