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LLM-informed planning framework enhances object search in unknown environments

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new LLM-informed model-based planning framework designed for object search in partially-known environments. This approach utilizes a large language model to estimate the probability of finding an object in various locations, which, combined with travel costs, informs the planning process for effective search. The system also incorporates a prompt selection method that allows for rapid selection of the best prompts and LLMs during deployment, leading to improved performance and reduced costs compared to baseline strategies. Experiments in simulation and on a real robot have demonstrated the effectiveness of this LLM-informed planning framework. AI

IMPACT This research could lead to more efficient and adaptable robotic systems capable of navigating and searching complex, partially understood environments.

RANK_REASON The cluster contains a research paper detailing a novel LLM-informed planning framework for object search. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM-informed planning framework enhances object search in unknown environments

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Abhishek Paudel, Abhish Khanal, Raihan I. Arnob, Shahriar Hossain, Gregory J. Stein ·

    Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection

    arXiv:2603.23800v2 Announce Type: replace-cross Abstract: We present a novel LLM-informed model-based planning framework, and a novel prompt selection method, for object search in partially-known environments. Our approach uses an LLM to estimate statistics about the likelihood o…

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