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In this paper, we introduce a novel learning-based approach for graspingknown rigid objects in highly cluttered scenes and precisely placing them basedon depth images. Our Placement Quality Network (PQ-Net) estimates the objectpose and the quality for each automatically generated grasp pose for multipleobjects simultaneously at 92 fps in a single forward pass of a neural network.All grasping and placement trials are executed in a physics simulation and thegained experience is transferred to the real world using domain randomization.We demonstrate that our policy successfully transfers to the real world. PQ-Netoutperforms other model-free approaches in terms of grasping success rate andautomatically scales to new objects of arbitrary symmetry without any humanintervention.

Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenes