[2603.23800] Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection

Computer Science > Robotics arXiv:2603.23800 (cs) [Submitted on 25 Mar 2026] Title:Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection Authors:Abhishek Paudel, Abhish Khanal, Raihan I. Arnob, Shahriar Hossain, Gregory J. Stein View a PDF of the paper titled Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection, by Abhishek Paudel and 4 other authors View PDF HTML (experimental) 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 of finding the target object when searching various locations throughout the scene that, combined with travel costs extracted from the environment map, are used to instantiate a model, thus using the LLM to inform planning and achieve effective search performance. Moreover, the abstraction upon which our approach relies is amenable to deployment-time model selection via the recent offline replay approach, an insight we leverage to enable fast prompt and LLM selection during deployment. Simulation experiments demonstrate that our LLM-informed model-based planning approach outperforms the baseline planning strategy that fully relies on LLM and optimistic strategy with as much as 11.8% and 39.2% improvements respectively, and our bandit-like selection approach enables quick ...