LLM-State: Open World State Representation for Long-horizon Task Planning with Large Language Model
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917646304280576 |
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| author | Chen, Siwei Xiao, Anxing Hsu, David |
| author_facet | Chen, Siwei Xiao, Anxing Hsu, David |
| contents | This work addresses the problem of long-horizon task planning with the Large Language Model (LLM) in an open-world household environment. Existing works fail to explicitly track key objects and attributes, leading to erroneous decisions in long-horizon tasks, or rely on highly engineered state features and feedback, which is not generalizable. We propose an open state representation that provides continuous expansion and updating of object attributes from the LLM's inherent capabilities for context understanding and historical action reasoning. Our proposed representation maintains a comprehensive record of an object's attributes and changes, enabling robust retrospective summary of the sequence of actions leading to the current state. This allows continuously updating world model to enhance context understanding for decision-making in task planning. We validate our model through experiments across simulated and real-world task planning scenarios, demonstrating significant improvements over baseline methods in a variety of tasks requiring long-horizon state tracking and reasoning. (Video\footnote{Video demonstration: \url{https://youtu.be/QkN-8pxV3Mo}.}) |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_17406 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LLM-State: Open World State Representation for Long-horizon Task Planning with Large Language Model Chen, Siwei Xiao, Anxing Hsu, David Robotics Artificial Intelligence This work addresses the problem of long-horizon task planning with the Large Language Model (LLM) in an open-world household environment. Existing works fail to explicitly track key objects and attributes, leading to erroneous decisions in long-horizon tasks, or rely on highly engineered state features and feedback, which is not generalizable. We propose an open state representation that provides continuous expansion and updating of object attributes from the LLM's inherent capabilities for context understanding and historical action reasoning. Our proposed representation maintains a comprehensive record of an object's attributes and changes, enabling robust retrospective summary of the sequence of actions leading to the current state. This allows continuously updating world model to enhance context understanding for decision-making in task planning. We validate our model through experiments across simulated and real-world task planning scenarios, demonstrating significant improvements over baseline methods in a variety of tasks requiring long-horizon state tracking and reasoning. (Video\footnote{Video demonstration: \url{https://youtu.be/QkN-8pxV3Mo}.}) |
| title | LLM-State: Open World State Representation for Long-horizon Task Planning with Large Language Model |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2311.17406 |