Using Language Models as Closed-Loop High-Level Planners for Robotics Applications: A Brief Overview and Benchmarks
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914508610469888 |
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| author | Wang, Hao Karnik, Sathwik Lim, Bea Bansal, Somil |
| author_facet | Wang, Hao Karnik, Sathwik Lim, Bea Bansal, Somil |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07410 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Using Language Models as Closed-Loop High-Level Planners for Robotics Applications: A Brief Overview and Benchmarks Wang, Hao Karnik, Sathwik Lim, Bea Bansal, Somil Robotics Artificial Intelligence 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 |
| title | Using Language Models as Closed-Loop High-Level Planners for Robotics Applications: A Brief Overview and Benchmarks |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2511.07410 |