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://github.com/Kamalnl92/Self-Supervised-Learning-for-pushing-and-grasping.

翻译：机器人常面临需要抓取目标物体但因其他物体阻碍而无法实施抓取动作的场景。针对此问题，本文提出一种面向高度杂乱环境下的深度强化学习方法，通过学习抓取与推挤策略实现目标物体操控。具体而言，我们提出双强化学习模型方法，在处理复杂场景时展现出高鲁棒性，在仿真环境中使用基础物体时任务完成率平均达98%。为评估所提方法的性能，我们在密集物体场景与堆积物体场景下共开展两组大规模实验，累计完成1000次仿真测试。实验结果表明，所提方法在两类场景中均表现优异，且优于近期最先进方法。为保障结果可复现性，我们公开了演示视频、训练模型及源代码：https://github.com/Kamalnl92/Self-Supervised-Learning-for-pushing-and-grasping。