ToBI – Team of Bielefeld: Enhancing Robot Behaviors and the Role of Multi-robotics in RoboCup@Home

  • Sebastian Meyer zu BorgsenEmail author
  • Timo Korthals
  • Florian Lier
  • Sven Wachsmuth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9776)


In this paper, we describe the joint effort of the Team of Bielefeld (ToBI) which won the RoboCup@Home competition in Leipzig 2016. RoboCup@Home consists of a defined set of benchmarking tests that cover multiple skills needed by service robots. We present the robotic platforms, technical contributions, and lessons learned from previous events that led to the final success this year. This includes a framework for behavior modeling and communication employed on two human-sized robots Floka and Biron as well as on the small robotic device AMiRo. These were used for a multi-robot collaboration scenario in the Finals. We describe our main contributions in automated testing, error handling, memorization and reporting, robot-robot coordination, and flexible grasping that considers object shape.



This research/work was supported by the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).

Thanks to the student team members of 2016 Marvin Barther, Julian Exner, Jonas Gerlach, Johannes Kummert, Luca Michael Lach, Henri Neumann, Nils Neumann, Leroy Rügemer, Tobias Schumacher, Dominik Sixt.


  1. 1.
    Wachsmuth, S., Holz, D., Rudinac, M., Ruiz-del Solar, J.: RoboCup@Home - benchmarking domestic service robots. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 4328–4329. AAAI Press (2015)Google Scholar
  2. 2.
    Wrede, B., Kleinehagenbrock, M., Fritsch, J.: Towards an integrated robotic system for interactive learning in a social context. In: Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems - IROS 2006, Bejing (2006)Google Scholar
  3. 3.
    Lohse, M., Siepmann, F., Wachsmuth, S.: A modeling framework for user-driven iterative design of autonomous systems. Int. J. Soc. Robot. 6(1), 121–139 (2014)CrossRefGoogle Scholar
  4. 4.
    Amigoni, F., Reggiani, M., Schiaffonati, V.: An insightful comparison between experiments in mobile robotics and in science. Auton. Robots 27(4), 313–325 (2009)CrossRefGoogle Scholar
  5. 5.
    Meyer zu Borgsen, S., Korthals, T., Wachsmuth, S.: ToBI-Team of Bielefeld The Human-Robot Interaction System for RoboCup@Home 2016 (2016)Google Scholar
  6. 6.
    Lier, F., Hanheide, M., Natale, L., Schulz, S., Weisz, J., Wachsmuth, S., Wrede, S.: Towards automated system and experiment reproduction in robotics. In: Burgard, W. (ed.) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2016)Google Scholar
  7. 7.
    Lier, F., Lütkebohle, I., Wachsmuth, S.: Towards automated execution and evaluation of simulated prototype HRI experiments. In: HRI 2014 Proceedings of the 2014 ACM/IEEE International Conference On Human-robot Interaction, pp. 230–231. ACM (2014)Google Scholar
  8. 8.
    Wienke, J., Wrede, S.: A middleware for collaborative research in experimental robotics. In: IEEE/SICE International Symposium on System Integration (SII2011), pp. 1183–1190. IEEE (2011)Google Scholar
  9. 9.
    Siepmann, F., Ziegler, L., Kortkamp, M., Wachsmuth, S.: Deploying a modeling framework for reusable robot behavior to enable informed strategies for domestic service robots. Robot. Auton. Syst. 63, 619–631 (2012)Google Scholar
  10. 10.
    Holthaus, P., Leichsenring, C., Bernotat, J., Richter, V., Pohling, M., Carlmeyer, B., Köster, N., Meyer zu Borgsen, S., Zorn, R., Schiffhauer, B., Engelmann, K.F., Lier, F., Schulz, S., Cimiano, P., Eyssel, F.A., Hermann, T., Kummert, F., Schlangen, D., Wachsmuth, S., Wagner, P., Wrede, B., Wrede, S.: How to address smart homes with a social robot? A multi-modal corpus of user interactions with an intelligent environment. In: Language Resources and Evaluation Conference, European Language Resources Association (ELRA) (2016)Google Scholar
  11. 11.
    Richter, V., Carlmeyer, B., Lier, F., Meyer zu Borgsen, S., Kummert, F., Wachsmuth, S., Wrede, B.: Are you talking to me? Improving the robustness of dialogue systems in a multi party HRI scenario by incorporating gaze direction and lip movement of attendees. In: Proceedings of the Fourth International Conference on Human-agent Interaction. ACM Digital Library (2016)Google Scholar
  12. 12.
    Herbrechtsmeier, S., Korthals, T., Schöpping, T., Rückert, U.: A modular & customizable open-source mini robot platform. In: 20th International Conference on Systems Theory, Control and Computing (ICSTCC), SINAIA, Romania (2016)Google Scholar
  13. 13.
    Amir, Y., Danilov, C., Miskin-Amir, M., Schultz, J., Stanton, J.: The spread toolkit: architecture and performance. Technical report (2004)Google Scholar
  14. 14.
    Koubaa, A.: Robot Operating System (ROS): The Complete Reference, vol. 1. Springer International Publishing, Heidelberg (2016). Scholar
  15. 15.
    Siepmann, F., Wachsmuth, S.: A modeling framework for reusable social behavior. In: De Silva, R., Reidsma, D. (eds.) Work in Progress Workshop Proceedings ICSR 2011, pp. 93–96. Springer, Amsterdam (2011)Google Scholar
  16. 16.
    Arkin, R.C.: Behavior-Based Robotics. Intelligent Robots and Autonomous Agents. The MIT Press, Cambridge (1998)Google Scholar
  17. 17.
    Chitta, S., Sucan, I., Cousins, S.: Moveit!. IEEE Robot. Autom. Mag. 19(1), 18–19 (2012)CrossRefGoogle Scholar
  18. 18.
    Haschke, R.: Grasping and manipulation of unknown objects based on visual and tactile feedback. In: Carbone, G., Gomez-Bravo, F. (eds.) Motion and Operation Planning of Robotic Systems. MMS, vol. 29, pp. 91–109. Springer, Cham (2015). Scholar
  19. 19.
    Ziegler, L.: The attentive robot companion: learning spatial information from observation and verbal interaction. Ph.D. thesis (2015)Google Scholar
  20. 20.
    Dondrup, C., Bellotto, N., Jovan, F., Hanheide, M.: Real-time multisensor people tracking for human-robot spatial interaction. In: Workshop on Machine Learning for Social Robotics at International Conference on Robotics and Automation (ICRA), ICRA/IEEE (2015)Google Scholar
  21. 21.
    Lier, F., Wienke, J., Nordmann, A., Wachsmuth, S., Wrede, S.: The cognitive interaction toolkit – improving reproducibility of robotic systems experiments. In: Brugali, D., Broenink, J.F., Kroeger, T., MacDonald, B.A. (eds.) SIMPAR 2014. LNCS, vol. 8810, pp. 400–411. Springer, Cham (2014). Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sebastian Meyer zu Borgsen
    • 1
    Email author
  • Timo Korthals
    • 1
  • Florian Lier
    • 1
  • Sven Wachsmuth
    • 1
  1. 1.Exzellenzcluster Cognitive Interaction Technology (CITEC)Bielefeld UniversityBielefeldGermany

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