Three years of the RoboCup standard platform league drop-in player competition

Creating and maintaining a large scale ad hoc teamwork robotics competition


The Standard Platform League is one of the main competitions at the annual RoboCup world championships. In this competition, teams of five humanoid robots play soccer against each other. In 2013, the league began a new competition which serves as a testbed for cooperation without pre-coordination: the Drop-in Player Competition. Instead of homogeneous robot teams that are each programmed by the same people and hence implicitly pre-coordinated, this competition features ad hoc teams, i.e. teams that consist of robots originating from different RoboCup teams and as such running different software. In this article, we provide an overview of this competition, including its motivation, rules, and how these rules have changed across three iterations of the competition. We then present and analyze the strategies utilized by various drop-in players as well as the results of the first three competitions before suggesting improvements for future competitive evaluations of ad hoc teamwork. To the best of our knowledge, these three competitions are the largest annual ad hoc teamwork robotic experiment to date. Across three years, the competition has seen 56 entries from 30 different organizations and consisted of 510 min of game time that resulted in approximately 85 robot hours.

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Fig. 1
Fig. 2


  1. 1.

  2. 2.

  3. 3.

  4. 4.

  5. 5.

  6. 6.

    MacAlpine, P. (2014). Private communication.

  7. 7.


  1. 1.

    Albrecht, S. V. (2015). Utilising policy types for effective ad hoc coordination in multiagent systems. Ph.D. thesis, The University of Edinburgh, Edinburgh.

  2. 2.

    Barrett, S. (2014). Making friends on the fly: Advances in ad hoc teamwork. Ph.D. thesis, The University of Texas at Austin, Austin, TX.

  3. 3.

    Bowling, M. & McCracken, P. (2005). Coordination and adaptation in impromptu teams. In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI’05), Pittsburgh, PA.

  4. 4.

    Dias, B. (2004). Traderbots: A new paradigm for robust and efficient multirobot coordination in dynamic environments. Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA.

  5. 5.

    Grosz, B. J., & Kraus, S. (1996). Collaborative plans for complex group action. Artificial Intelligence, 86(2), 269–357.

    MathSciNet  Article  Google Scholar 

  6. 6.

    Jones, E., Browning, B., Dias, M. B., Argall, B., Veloso, M. M., & Stentz, A. T. (2006). Dynamically formed heterogeneous robot teams performing tightly-coordinated tasks. Proceedings of the 2006 IEEE International Conference on Robotics and Automation (ICRA’06) (pp. 570–575), Orlando, FL.

  7. 7.

    Kitano, H., & Asada, M. (1998). RoboCup humanoid challenge: That’s one small step for a robot, one giant leap for mankind. Proceedings of the 1998 IEEE/RSJ International conference on intelligent robots and systems (IROS’98) (pp. 419–424), Victoria, BC.

  8. 8.

    Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., & Osawa, E. (1997). Robocup: The robot world cup initiative. Proceedings of the first international conference on autonomous agents, AGENTS ’97 (pp. 340–347). ACM, New York.

  9. 9.

    Liemhetcharat, S. (2013). Representation, planning, and learning of dynamic ad hoc robot teams. Ph.D. Thesis, Carnegie Mellon University, Pittsburgh, PA.

  10. 10.

    MacAlpine, P., Genter, K., Barrett, S. & Stone, P. (2014). The RoboCup 2013 drop-in player challenges: Experiments in ad hoc teamwork. In Proceedings of the 2014 IEEE/RSJ international conference on intelligent robots and systems (IROS’14), Chicago, IL.

  11. 11.

    RoboCup Small Size Robot League: Small Size League/RoboCup 2015/Technical Challenges (2015). Retrieved from

  12. 12.

    RoboCup Technical Committee: Technical challenges for the RoboCup 2013 Standard Platform League competition (2013). Retrieved from

  13. 13.

    RoboCup Technical Committee: 2014 drop-in player strategies (2014). Retrieved from

  14. 14.

    RoboCup Technical Committee: RoboCup Standard Platform League (NAO) rule book (2014). Retrieved from

  15. 15.

    RoboCup Technical Committee: 2015 drop-in player strategies (2015). Retrieved from

  16. 16.

    RoboCup Technical Committee: RoboCup Standard Platform League (NAO) rule book (2015). Retrieved from

  17. 17.

    Röfer, T., Laue, T. (2014). On B-Human’s code releases in the standard platform league—software architecture and impact. In RoboCup 2013: Robot Soccer World Cup XVII, Lecture Notes in Artificial Intelligence, vol. 8371, pp. 648–656. Berlin: Springer.

  18. 18.

    Röfer, T., Laue, T., Müller, J., Schüthe, D., Böckmann, A., Jenett, D., Koralewski, S., Maaß, F., Maier, E., Siemer, C., Tsogias, A. & Vosteen, J. B. (2014). B-human team report and code release 2014. Retrieved from

  19. 19.

    Röfer, T., Laue, T., Richter-Klug, J., Schünemann, M., Stiensmeier, J., Stolpmann, A., Stöwing, A. & Thielke, F. (2015). B-Human team report and code release 2015. Retrieved from

  20. 20.

    Stone, P., Kaminka, G., Kraus, S., Rosenschein, J., & Agmon, N. (2013). Teaching and leading an ad hoc teammate: Collaboration without pre-coordination. Artificial Intelligence, 203, 35–65.

    MathSciNet  Article  MATH  Google Scholar 

  21. 21.

    Stone, P., Kaminka, G. A., Kraus, S. & Rosenschein, J. S. (2010). Ad hoc autonomous agent teams: Collaboration without pre-coordination. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI’10), Atlanta, GA.

  22. 22.

    Stone, P., & Veloso, M. (1999). Task decomposition, dynamic role assignment, and low-bandwidth communication for real-time strategic teamwork. AIJ, 110(2), 241–273.

    MATH  Google Scholar 

  23. 23.

    Tambe, M. (1997). Towards flexible teamwork. Artificial Intelligence Research, 7(1), 83–124.

    Google Scholar 

  24. 24.

    Wu, F., Zilberstein, S. & Chen, X. (2011) Online planning for ad hoc autonomous agent teams. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI’11) (pp. 439–445), Barcelona.

  25. 25.

    Wurman, P. R., D’Andrea, R., & Mountz, M. (2008). Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Magazine, 29(1), 9–19.

    Google Scholar 

  26. 26.

    Zickler, S., Laue, T., Birbach, O., Wongphati, M., & Veloso, M. (2010). Ssl-vision: The shared vision system for the robocup small size league. RoboCup 2009: Robot Soccer World Cup XIII (Vol. 5949, pp. 425–436), Lecture Notes in Computer Science Berlin Heidelberg: Springer.

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Katie Genter and Peter Stone are part of the Learning Agents Research Group (LARG) at UT Austin. LARG research is supported in part by NSF (CNS-1330072, CNS-1305287), ONR (21C184-01), AFRL (FA8750-14-1-0070), and AFOSR (FA9550-14-1-0087).

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Correspondence to Katie Genter.

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Genter, K., Laue, T. & Stone, P. Three years of the RoboCup standard platform league drop-in player competition. Auton Agent Multi-Agent Syst 31, 790–820 (2017).

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  • Ad hoc teamwork
  • Coordination
  • Multiagent teamwork
  • RoboCup
  • Robot soccer