Large-Language-Model-Guided State Estimation for Partially Observable Task and Motion Planning
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911489768554496 |
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| author | Kim, Yoonwoo Arora, Raghav Martín-Martín, Roberto Stone, Peter Abbatematteo, Ben Sung, Yoonchang |
| author_facet | Kim, Yoonwoo Arora, Raghav Martín-Martín, Roberto Stone, Peter Abbatematteo, Ben Sung, Yoonchang |
| contents | Robot planning in partially observable environments, where not all objects are known or visible, is a challenging problem, as it requires reasoning under uncertainty through partially observable Markov decision processes. During the execution of a computed plan, a robot may unexpectedly observe task-irrelevant objects, which are typically ignored by naive planners. In this work, we propose incorporating two types of common-sense knowledge: (1) certain objects are more likely to be found in specific locations; and (2) similar objects are likely to be co-located, while dissimilar objects are less likely to be found together. Manually engineering such knowledge is complex, so we explore leveraging the powerful common-sense reasoning capabilities of large language models (LLMs). Our planning and execution framework, CoCo-TAMP, introduces a hierarchical state estimation that uses LLM-guided information to shape the belief over task-relevant objects, enabling efficient solutions to long-horizon task and motion planning problems. In experiments, CoCo-TAMP achieves an average reduction of 62.7% in planning and execution time in simulation, and 72.6% in real-world demonstrations, compared to a baseline that does not incorporate either type of common-sense knowledge. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_03704 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Large-Language-Model-Guided State Estimation for Partially Observable Task and Motion Planning Kim, Yoonwoo Arora, Raghav Martín-Martín, Roberto Stone, Peter Abbatematteo, Ben Sung, Yoonchang Robotics Artificial Intelligence Robot planning in partially observable environments, where not all objects are known or visible, is a challenging problem, as it requires reasoning under uncertainty through partially observable Markov decision processes. During the execution of a computed plan, a robot may unexpectedly observe task-irrelevant objects, which are typically ignored by naive planners. In this work, we propose incorporating two types of common-sense knowledge: (1) certain objects are more likely to be found in specific locations; and (2) similar objects are likely to be co-located, while dissimilar objects are less likely to be found together. Manually engineering such knowledge is complex, so we explore leveraging the powerful common-sense reasoning capabilities of large language models (LLMs). Our planning and execution framework, CoCo-TAMP, introduces a hierarchical state estimation that uses LLM-guided information to shape the belief over task-relevant objects, enabling efficient solutions to long-horizon task and motion planning problems. In experiments, CoCo-TAMP achieves an average reduction of 62.7% in planning and execution time in simulation, and 72.6% in real-world demonstrations, compared to a baseline that does not incorporate either type of common-sense knowledge. |
| title | Large-Language-Model-Guided State Estimation for Partially Observable Task and Motion Planning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2603.03704 |