用于状态落地任务规划的领域条件场景图
Domain-Conditioned Scene Graphs for State-Grounded Task Planning
AI总结：
针对大型多模态模型在机器人任务规划中状态落地能力不足的问题，提出以领域条件场景图为核心的结构化状态落地框架，可映射为PDDL符号状态，采用轻量视觉-语言方法生成，在三个领域上的准确率和成功率均显著优于现有LMM方法。
摘要：
近期的机器人任务规划框架已集成GPT-4o等大型多模态模型（LMM）。为解决这类模型的落地问题，已有研究提出将流程拆分为感知状态落地和后续的基于状态的规划。我们在本研究中发现，基于LMM的方法的状态落地能力仍受限于细粒度、结构化、领域特定的场景理解能力不足。为弥补这一缺陷，我们开发了一种更具结构化的状态落地框架，其核心场景表示为领域条件场景图。我们证明这种表示天然具备可执行性，可直接映射为规划领域定义语言（PDDL）等规划语言中的符号状态。我们给出了该状态落地框架的一个实例，其中领域条件场景图的生成采用轻量级视觉-语言方法实现，即在领域相关目标检测的基础上对领域特定谓词进行分类。在三个领域上的评估结果显示，与基于LMM的方法相比，我们的方法实现了显著更高的状态落地准确率和任务规划成功率。
英文摘要：
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.
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