Anticipate & Act: Integrating LLMs and Classical Planning for Efficient Task Execution in Household Environments. In the IEEE International Conference on Robotics and Automation

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publication2024 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Number of pages8
Publication statusAccepted/In press - 31 Jan 2024
Event2024 IEEE International Conference on Robotics and Automation - Yokohama, Japan
Duration: 13 May 202417 May 2024
https://2024.ieee-icra.org/

Conference

Conference2024 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2024
Country/TerritoryJapan
CityYokohama
Period13/05/2417/05/24
Internet address

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