发表机构
University of Innsbruck（因斯布鲁克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对传统TAMP方法在接触密集型任务中规划时间长、成功率低的问题，提出结合VLM与U-TAMP的方法，在模拟厨房场景中实现了更高的规划成功率与更快的规划速度。
AI 中文摘要
传统任务与运动规划（TAMP）方法主要聚焦于定义动作序列以及执行长 horizon 任务所需的几何与运动学约束，然而其在现实场景中的适用性有限，因为它们通常采用简化的物体模型，忽略了接触密集型任务成功执行所需的关键物理属性。此外，这些方法在运动规划阶段常采用亚符号推理，这会大幅增加规划时间并降低整体成功率。我们提出一种利用 TAMP 方法的方案，定义以物体为中心的执行约束抽象，称为统一 TAMP（U-TAMP），用于执行涉及形状、尺寸、材料各异的物体间交互的机器人任务。我们利用视觉语言模型（VLM）生成物体间可供性的抽象，以表征接触密集型任务中物体间的物理交互约束，例如抓取和支撑约束。这些约束被用于丰富 U-TAMP 规划域，以处理具有可变物理属性的物体。我们在模拟厨房桌面整理场景中开展实验，将结果与原始 U-TAMP 以及利用物体可供性常识知识进行规划生成的最先进 VLM 基规划器进行对比。与其他方法相比，我们的方案实现了显著更高的规划成功率，并将规划时间缩短了1至2个数量级。
英文摘要
Traditional task-and-motion planning (TAMP) approaches primarily focus on defining sequences of actions along with the necessary geometric and kinematic constraints to execute long-horizon tasks. However, their applicability in real-world settings is limited, as they typically assume simplified object models that overlook key physical properties critical for the successful execution of contact-rich tasks. Moreover, they often use sub-symbolic reasoning during motion planning, which drastically increases planning time and decreases overall success rates. We propose a method that leverages a TAMP approach, defining object-centric abstractions of execution constraints, called Unified TAMP (U-TAMP), to execute robotic tasks involving interactions among objects with heterogeneous shapes, sizes, and materials. Using a Vision-Language Model (VLM), we generate abstractions of inter-object affordances for characterizing physical interaction constraints between objects in contact-rich tasks, such as grasp and support constraints. These constraints are used to enrich the U-TAMP planning domain to deal with objects with variable physical properties. We perform experiments in simulated kitchen table organization scenarios and compare our results with those of the original U-TAMP, as well as a state-of-the-art VLM-based planner that leverages common sense knowledge of objects' affordances for plan generation. Our approach achieves significantly higher planning success rates and improves planning times by one to two orders of magnitude compared to other methods.
