Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments
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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 98 environment. To evaluate the performance of the proposed approach, we performed two extensive sets of experiments in packed objects and a pile of objects scenarios. Experimental results showed that the proposed method worked very well in both scenarios and outperformed the recent state-of-the-art approaches
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