Domain-Conditioned Scene Graphs for State-Grounded Task Planning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915470575140864 |
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| author | Herzog, Jonas Liu, Jiangpin Wang, Yue |
| author_facet | Herzog, Jonas Liu, Jiangpin Wang, Yue |
| contents | Recent robotic task planning frameworks have integrated large multimodal models (LMMs) such as GPT-4o. To address grounding issues of such models, it has been suggested to split the pipeline into perceptional state grounding and subsequent state-based planning. As we show in this work, the state grounding ability of LMM-based approaches is still limited by weaknesses in granular, structured, domain-specific scene understanding. To address this shortcoming, we develop a more structured state grounding framework that features a domain-conditioned scene graph as its scene representation. We show that such representation is actionable in nature as it is directly mappable to a symbolic state in planning languages such as the Planning Domain Definition Language (PDDL). We provide an instantiation of our state grounding framework where the domain-conditioned scene graph generation is implemented with a lightweight vision-language approach that classifies domain-specific predicates on top of domain-relevant object detections. Evaluated across three domains, our approach achieves significantly higher state rounding accuracy and task planning success rates compared to LMM-based approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain-Conditioned Scene Graphs for State-Grounded Task Planning Herzog, Jonas Liu, Jiangpin Wang, Yue Robotics Recent robotic task planning frameworks have integrated large multimodal models (LMMs) such as GPT-4o. To address grounding issues of such models, it has been suggested to split the pipeline into perceptional state grounding and subsequent state-based planning. As we show in this work, the state grounding ability of LMM-based approaches is still limited by weaknesses in granular, structured, domain-specific scene understanding. To address this shortcoming, we develop a more structured state grounding framework that features a domain-conditioned scene graph as its scene representation. We show that such representation is actionable in nature as it is directly mappable to a symbolic state in planning languages such as the Planning Domain Definition Language (PDDL). We provide an instantiation of our state grounding framework where the domain-conditioned scene graph generation is implemented with a lightweight vision-language approach that classifies domain-specific predicates on top of domain-relevant object detections. Evaluated across three domains, our approach achieves significantly higher state rounding accuracy and task planning success rates compared to LMM-based approaches. |
| title | Domain-Conditioned Scene Graphs for State-Grounded Task Planning |
| topic | Robotics |
| url | https://arxiv.org/abs/2504.06661 |