{"ID":2844146,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07410","arxiv_id":"2511.07410","title":"Using Language Models as Closed-Loop High-Level Planners for Robotics Applications: A Brief Overview and Benchmarks","abstract":"Large Language Models (LLMs) and Vision Language Models (VLMs) have become popular tools for embodied high-level planning. However, their deployment in black-box settings often leads to unpredictable or costly errors. To harness their capabilities more reliably in robotic systems, we empirically investigate practical strategies for integrating language models as closed-loop planners. Concretely, we study how the control horizon and warm-starting impact the performance of language model-based planners. We design and conduct controlled experiments to extract actionable insights, providing recommendations that can help improve the performance and robustness of language model-based embodied planning. The full implementation and experiments are available on the project website","short_abstract":"Large Language Models (LLMs) and Vision Language Models (VLMs) have become popular tools for embodied high-level planning. However, their deployment in black-box settings often leads to unpredictable or costly errors. To harness their capabilities more reliably in robotic systems, we empirically investigate practical s...","url_abs":"https://arxiv.org/abs/2511.07410","url_pdf":"https://arxiv.org/pdf/2511.07410v2","authors":"[\"Hao Wang\",\"Sathwik Karnik\",\"Bea Lim\",\"Somil Bansal\"]","published":"2025-11-10T18:56:56Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}