Learning a High-Precision Robotic Assembly Task Using Pose Estimation from Simulated Depth Images

Yuval Litvak, Armin Biess, Aharon Bar-Hillel

Research output: Working paper/PreprintPreprint


Most of industrial robotic assembly tasks today require fixed initial conditions for successful assembly. These constraints induce high production costs and low adaptability to new tasks. In this work we aim towards flexible and adaptable
robotic assembly by using 3D CAD models for all parts to be assembled. We focus on a generic assembly task - the Siemens Innovation Challenge - in which a robot needs to assemble a gear like mechanism with high precision into an operating system. To obtain the millimeter-accuracy required for this task and
industrial settings alike, we use a depth camera mounted near the robot’s end-effector. We present a high-accuracy three-stage pose estimation pipeline based on deep convolutional neural networks, which includes detection, pose estimation, refinement, and handling of near- and full symmetries of parts. The
networks are trained on simulated depth images by means to ensure successful transfer to the real robot. We obtain an average pose estimation error of 2.14 millimeters and 1.09degree leading to 88.6% success rate for robotic assembly of randomly distributed parts. To the best of our knowledge, this is the first time that the Siemens Innovation Challenge is fully solved, opening up new possibilities for automated industrial assembly.
Original languageEnglish GB
StatePublished - 2018

Publication series

NameArXiv abs/1809.10699


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