• 2x ABB IRB 140
• 1x Adept Cobra 600
• 2x Universal Robots UR10 and 1x UR5
• 2x KUKA uBot, 1x KR 60-2 and 1x KR 3
• 1x Stäubli RX60 and 1x TX60L
• 1x Schneider Electric P4s-F PacDrive Delta robot
• 1x Visualeyez Motion Tracker VZ2000
• 1x Immersion MicroScribe 3DX
• 1x Festo Robotino
• 2x uFactory uArm
• Workshop with Ultimaker 3, Ultimaker 2, WABECO D6000E lathe, Optimum OPTImill MH 28V milling and drilling machine, ADAM MC 22 pillar drilling machine

• EASY-ROB 3D Robot Simulation Tool
• V-REP Virtual Robot Experimentation Platform
• MATLAB

Using Uncertainty Quantification in 6DoF Pose Estimation to improve Gripper Performance in Robotic Applications

Abstract: Implementing robotic manipulation robustly in a real-world setting requires accurate and robust 6-DoF object pose estimations. One of the critical chal-lenges of implementing this task using deep neural networks is their inac-curacies when dealing with inputs outside the training domain. Therefore efficient and robust uncertainty quantification in pose estimators is critical. We implement a 6-DoF object pose estimation application that solves a children toy using a robot equipped with a parallel gripper. The task of the toy is to pick up known objects of different geometric shapes and insert them into a matching shape in a container. The openings in the container are only a few millimetres larger than the corresponding object. Due to this slim margin for error, we require high-quality pose predictions. Using the application as an example, the gripper performance is optimised through uncertainty quantification. We estimate predictive uncertainty using ensembles. Three different ensemble generation methods based on changing the members weights, changing the members architectures, and changing the training data sources of the members are being tested. In multiple experi-ments, we are able to eliminate false positive detections completely. Using the ensembles, we can detect the objects in 87% of the attempts with an ADD error below 1 cm. In real-world scenes with three images from differ-ent viewpoints, we successfully identify the best resulting pose estimations using uncertainty quantifications in most examples.