Usually, grasping in a totebox always encounters bottlenecks when the object is at the edge or even at the corner of the totebox. Meanwhile, if the objects are stacked in a pile, there may be no grasps to be selected. In this paper, an algorithm based on deep reinforcement learning is applied to combine grasping with pushing to deal with these cases. In order to make sure that a push must increase grasp access, we propose to apply the changes of grasp’s quality function combined with forgetting mechanism to promote a pushing action. Moreover, a double experience replay is set up to increase the search on the boundaries. To make a balance between efficiency and robustness, the traditional policy is improved using acceptance thresholds and with 99% precision. Our algorithm is trained in a simulation environment using YCB object dataset and finally is transferred into a real-world environment. In our experiment, our algorithm achieves the best results both in simulation and real world (with 86.67% completion for YCB objects and 83.37% completion for novel objects) compared to other famous works. GRAPHICAL ABSTRACT

中文翻译：

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从模拟中学习手提箱中的有效推送和抓取策略

通常，当物体位于手提箱的边缘甚至角落时，在手提箱中抓取总是会遇到瓶颈。同时，如果对象堆叠成一堆，则可能没有要选择的抓握。本文采用一种基于深度强化学习的算法，将抓与推相结合来处理这些情况。为了确保推送必须增加抓取访问，我们建议应用抓取质量函数的变化结合遗忘机制来促进推送动作。此外，还设置了双重体验回放，以增加对边界的搜索。为了在效率和稳健性之间取得平衡，传统策略使用接受阈值进行了改进，精度为 99%。我们的算法在使用 YCB 对象数据集的模拟环境中进行训练，最后转移到现实环境中。在我们的实验中，与其他著名作品相比，我们的算法在模拟和现实世界中均取得了最佳结果（YCB 对象的完成率为 86.67%，新对象的完成率为 83.37%）。图形概要