Jiang Ping, Oaki Junji, Ishihara Yoshiyuki, Ooga Junichiro, Han Haifeng, Sugahara Atsushi, Tokura Seiji, Eto Haruna, Komoda Kazuma, Ogawa Akihito
Corporate Research & Development Center, Toshiba Corporation, Kawasaki, Japan.
Front Neurorobot. 2022 Mar 24;16:806898. doi: 10.3389/fnbot.2022.806898. eCollection 2022.
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).
深度学习已被广泛用于推断稳健的抓取。尽管最初使用人工标注的RGB-D数据集来学习抓取配置，但准备这种大型数据集成本高昂。为了解决这个问题，通过物理模拟器生成图像，并使用受物理启发的模型（例如，吸盘与物体之间的接触模型）作为抓取质量评估指标来标注合成图像。然而，这种接触模型很复杂，需要通过实验进行参数识别以确保在现实世界中的性能。此外，先前的研究没有考虑机械手的可达性，例如当具有高抓取质量的抓取配置由于碰撞或机器人的物理限制而无法到达目标时。在本研究中，我们提出了一种基于直观几何分析的抓取质量评估指标。我们进一步纳入了可达性评估指标。我们通过模拟器中提出的评估指标对合成图像进行逐像素的抓取质量和可达性标注，以训练一个名为吸力可抓取性U-Net++（SG-U-Net++）的自动编码器-解码器。实验结果表明，我们直观的抓取质量评估指标与受物理启发的指标具有竞争力。学习可达性有助于通过去除明显不可达的候选对象来减少运动规划计算时间。该系统实现了每小时560件的整体拾取速度。
