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Analysis & performance

Scientific publication

Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image Using CNN

Publication from Robotics
Cognitive Robotics

Justinas Miseikis, Inka Brijacak , Saeed Yahyanejad , Kyrre Glette, Ole Jakob Elle, Jim Torresen

EEE International Conference on Robotics and Automation (ICRA 2019), Montreal, Canada , 5/2019


Collaborative robots are becoming more commonon factory floors as well as regular environments, however,their safety still is not a fully solved issue. Collision detectiondoes not  always perform as expected and collision avoidance isstill an active research area. Collision avoidance works well forfixed robot-camera setups, however, if they are shifted around,Eye-to-Hand calibration becomes invalid making it difficultto accurately run many of the existing collision avoidancealgorithms. We approach the problem by presenting a stand-alone system capable of detecting the robot and estimatingits position, including individual joints, by using a simple 2Dcolour image as an input, where no Eye-to-Hand calibrationis needed. As an  extension of previous work, a two-stagetransfer learning approach is used to re-train a multi-objectiveconvolutional neural network (CNN) to allow it to be used withheterogeneous robot arms. Our method is capable of detectingthe robot in real-time and new robot types can be addedby having significantly smaller training datasets compared tothe requirements of a fully trained network. We present datacollection approach, the structure of the multi-objective CNN,the two-stage transfer learning training and test results by usingreal robots from Universal Robots, Kuka, and Franka Emika.Eventually, we analyse possible application areas of our methodtogether with the possible improvements.

Conference website