This paper presents Sim-Suction, a robust object-aware suction grasp policy for mobile manipulation platforms with dynamic camera viewpoints, designed to pick up unknown objects from cluttered environments. Suction grasp policies typically employ data-driven approaches, necessitating large-scale, accurately-annotated suction grasp datasets. However, the generation of suction grasp datasets in cluttered environments remains underexplored, leaving uncertainties about the relationship between the object of interest and its surroundings. To address this, we propose a benchmark synthetic dataset, Sim-Suction-Dataset, comprising 500 cluttered environments with 3.2 million annotated suction grasp poses. The efficient Sim-Suction-Dataset generation process provides novel insights by combining analytical models with dynamic physical simulations to create fast and accurate suction grasp pose annotations. We introduce Sim-Suction-Pointnet to generate robust 6D suction grasp poses by learning point-wise affordances from the Sim-Suction-Dataset, leveraging the synergy of zero-shot text-to-segmentation. Real-world experiments for picking up all objects demonstrate that Sim-Suction-Pointnet achieves success rates of 96.76%, 94.23%, and 92.39% on cluttered level 1 objects (prismatic shape), cluttered level 2 objects (more complex geometry), and cluttered mixed objects, respectively. The Sim-Suction policies outperform state-of-the-art benchmarks tested by approximately 21% in cluttered mixed scenes.

翻译：本文提出Sim-Suction，一种适用于动态相机视角的移动操作平台鲁棒性物体感知吸盘抓取策略，旨在从杂乱环境中抓取未知物体。吸盘抓取策略通常采用数据驱动方法，需要大规模、精确标注的吸盘抓取数据集。然而，杂乱环境下的吸盘抓取数据集生成尚未得到充分探索，目标物体与其周围环境的关系仍存在不确定性。为解决此问题，我们提出基准合成数据集Sim-Suction-Dataset，包含500个杂乱环境及320万个标注的吸盘抓取位姿。高效的Sim-Suction-Dataset生成过程通过结合分析模型与动态物理仿真，为快速、精确地生成吸盘抓取位姿标注提供了新思路。我们引入Sim-Suction-Pointnet，通过学习Sim-Suction-Dataset中的逐点可操作性，结合零样本文本分割的协同作用，生成鲁棒的六自由度吸盘抓取位姿。在拾取所有物体的实际实验中，Sim-Suction-Pointnet对一级杂乱（棱柱形）、二级杂乱（更复杂几何形状）及混合杂乱物体的成功率分别达到96.76%、94.23%和92.39%。Sim-Suction策略在混合杂乱场景中的性能较现有最优基准测试提升约21%。