Keywords: object search, planning, LLM, model-based planning
TL;DR: We augment LLM with classical planning to improve object search in unknown household environments.
Abstract: We enhance object search in unknown environments by integrating a Large Language Model (LLM) with model-based planning to quickly and reliably locate an object of interest. The LLM is prompted to produce predictions about the likelihood of finding the object of interest used to define a model for planning that the robot then uses to determine its search policy, affording both good performance due to the integration of learning and reliability due to the reliance on classical planning to find the object. From our findings on 200 random maps on the ProcTHOR dataset, our proposed LLM-informed planner, utilizing GPT-4o predictions, achieves a cost reduction of 27.1\% and 30.3\% compared to the standard baseline and Myopic LLM-informed baseline, respectively.
Submission Number: 50
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