Robots often face situations where grasping a goal object is desirable but not feasible due to other present objects preventing the grasp action. We present a deep Reinforcement Learning approach to learn grasping and pushing policies for manipulating a goal object in highly cluttered environments to address this problem. In particular, a dual Reinforcement Learning model approach is proposed, which presents high resilience in handling complicated scenes, reaching an average of 98% task completion using primitive objects in a simulation environment. To evaluate the performance of the proposed approach, we performed two extensive sets of experiments in packed objects and a pile of object scenarios with a total of 1000 test runs in simulation. Experimental results showed that the proposed method worked very well in both scenarios and outperformed the recent state-of-the-art approaches. Demo video, trained models, and source code for the results reproducibility purpose are publicly available. https://sites.google.com/view/pushandgrasp/home

翻译：机器人常面临因其他物体阻碍抓取动作而导致目标物体无法被成功抓取的情形。针对此问题，我们提出一种深度强化学习方法，用于学习在高度杂乱环境中操纵目标物体的抓取与推动策略。具体而言，我们提出了一种双强化学习模型方法，该方法在处理复杂场景时表现出高度鲁棒性，在仿真环境下使用原始物体的任务完成率平均达到98%。为评估所提方法的性能，我们分别在密集堆叠物体场景与散落物体堆场景中开展了两组大规模实验，共进行1000次仿真测试。实验结果表明，该方法在这两种场景下均表现出色，且优于近期最先进的已有方法。为便于结果复现，我们公开了演示视频、训练模型及源代码，访问地址为：https://sites.google.com/view/pushandgrasp/home