Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark

This article 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 6-D 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.