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homer@UniKoblenz: Winning Team of the RoboCup Virtual @Home Open Platform League 2021

Part of the Lecture Notes in Computer Science book series (LNAI,volume 13132)


In this paper we present our approaches for solving this year’s virtual RoboCup @Home tasks with special focus on object detection and manipulation in the simulation environment Gazebo, which lead to our successful participation in the Open Platform League. There, we deployed the model of robot TIAGo Steel Edition, which is publicly provided by PAL Robotics. For object detection and pose estimation, we use a custom pixel-based clustering approach for segmenting potentially object supporting planes first. Instances of arbitrary and unknown objects can thus be detected and suitable grasping poses can be deduced. We perform object recognition by training our YOLO v3 network on a synthetic dataset of rendered YCB object images with Blender. A generalized grasping pipeline, which integrates extent and orientation information from our general object detection, performs planning and execution of combined arm and torso trajectory using MoveIt.


  • RoboCup@Home
  • Open Platform League
  • Domestic service robotics
  • homer@UniKoblenz
  • TIAGo
  • Gazebo
  • Simulated robotics

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We want to thank the participating students Niklas Yann Wettengel, Robin Bartsch, Janine Buchholz, Maximilian Grothe and Kevin Fischer Rios. Also thanks to everybody in the RoboCup committees who have made RoboCup @Home in this pandemic possible despite all the obstacles and long working hours.

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Correspondence to Daniel Müller .

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Müller, D., Wettengel, N.Y., Paulus, D. (2022). homer@UniKoblenz: Winning Team of the RoboCup Virtual @Home Open Platform League 2021. In: Alami, R., Biswas, J., Cakmak, M., Obst, O. (eds) RoboCup 2021: Robot World Cup XXIV. RoboCup 2021. Lecture Notes in Computer Science(), vol 13132. Springer, Cham.

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