Deep learning has been widely used for inferring robust grasps. Although human-labeled RGB-D datasets were initially used to learn grasp configurations, preparation of this kind of large dataset is expensive. To address this problem, images were generated by a physical simulator, and a physically inspired model (e.g., a contact model between a suction vacuum cup and object) was used as a grasp quality evaluation metric to annotate the synthesized images. However, this kind of contact model is complicated and requires parameter identification by experiments to ensure real world performance. In addition, previous studies have not considered manipulator reachability such as when a grasp configuration with high grasp quality is unable to reach the target due to collisions or the physical limitations of the robot. In this study, we propose an intuitive geometric analytic-based grasp quality evaluation metric. We further incorporate a reachability evaluation metric. We annotate the pixel-wise grasp quality and reachability by the proposed evaluation metric on synthesized images in a simulator to train an auto-encoder--decoder called suction graspability U-Net++ (SG-U-Net++). Experiment results show that our intuitive grasp quality evaluation metric is competitive with a physically-inspired metric. Learning the reachability helps to reduce motion planning computation time by removing obviously unreachable candidates. The system achieves an overall picking speed of 560 PPH (pieces per hour).
翻译:深层学习被广泛用来推断稳健的图像。 虽然人类标记的 RGB-D 数据集最初用于学习抓取配置, 但这种大型数据集的制作费用昂贵。 要解决这个问题, 图像是由物理模拟器生成的, 物理激励模型( 例如吸尘真空杯和物体之间的接触模型) 被广泛用作一种对合成图像进行批注的预估质量评估指标。 然而, 这种接触模式复杂, 需要通过实验确定参数以确保真实的世界性能。 此外, 先前的研究没有考虑操纵或可达性, 如由于碰撞或机器人的物理限制, 高抓力配置无法达到目标。 在此研究中, 我们提出一个不直观的地理测量分析分析的抓取质量评估模型( 例如抽取合成图像的精选质量和可达性, 以确保真实世界性业绩。 前期研究没有考虑到高抓力的抓取性配置组合组合组合组合组合组合组合的可达性, 高超速预估定的精确性模型, 以软化的软化性模型为软化性模型。 软化的软化的软化的软化模型, 升级的升级的升级的计算结果, 将可达性模型升级的计算结果。