Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments

Abstract-- 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. Grasping is a crucial skill for many robotics applications. It further opens opportunities to robots to numerous skills that are not This paper mainly focuses on self-supervised learning for attainable without a versed object's grasp. In many scenarios, pushing and grasping objects in highly cluttered environments objects do not appear in isolation, and therefore, grasping via deep Reinforcement Learning (RL).