In this paper, we introduce a novel learning-based approach for grasping known rigid objects in highly cluttered scenes and precisely placing them based on depth images. Our Placement Quality Network (PQ-Net) estimates the object pose and the quality for each automatically generated grasp pose for multiple objects 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 the gained experience is transferred to the real world using domain randomization. We demonstrate that our policy successfully transfers to the real world. PQ-Net outperforms other model-free approaches in terms of grasping success rate and automatically scales to new objects of arbitrary symmetry without any human intervention.
@article{arxiv.2101.04781,
title = {Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenes},
author = {Kilian Kleeberger and Markus Völk and Marius Moosmann and Erik Thiessenhusen and Florian Roth and Richard Bormann and Marco F. Huber},
journal= {arXiv preprint arXiv:2101.04781},
year = {2021}
}
Comments
Accepted at 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)