Learning Pose Estimation for High-Precision Robotic Assembly Using Simulated Depth Images
This work addresses the need for flexible and adaptable robotic assembly in industrial settings, reducing costs and constraints, though it is incremental as it builds on existing pose estimation techniques.
The paper tackles the problem of high-precision robotic assembly by developing a two-stage deep learning pose estimation method using simulated depth images, achieving an average error of 2.16 mm and 0.64 degrees with a 91% success rate in assembling gear-like mechanisms.
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 end-effector. We present a high-accuracy two-stage pose estimation procedure 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 with means to ensure successful transfer to the real robot. We obtain an average pose estimation error of 2.16 millimeters and 0.64 degree leading to 91% 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 addressed, with all the parts assembled with high success rates.