Autonomous Agents and Multi-Agent Systems

, Volume 31, Issue 4, pp 790–820 | Cite as

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

Creating and maintaining a large scale ad hoc teamwork robotics competition
  • Katie GenterEmail author
  • Tim Laue
  • Peter Stone


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.


Ad hoc teamwork Coordination Multiagent teamwork RoboCup Robot soccer 



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).


  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.Google Scholar
  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.Google Scholar
  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.Google Scholar
  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.Google Scholar
  5. 5.
    Grosz, B. J., & Kraus, S. (1996). Collaborative plans for complex group action. Artificial Intelligence, 86(2), 269–357.MathSciNetCrossRefGoogle 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.Google Scholar
  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.Google Scholar
  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.Google Scholar
  9. 9.
    Liemhetcharat, S. (2013). Representation, planning, and learning of dynamic ad hoc robot teams. Ph.D. Thesis, Carnegie Mellon University, Pittsburgh, PA.Google Scholar
  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.Google Scholar
  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.Google Scholar
  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.MathSciNetCrossRefzbMATHGoogle 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.Google Scholar
  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.zbMATHGoogle 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.Google Scholar
  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.Google Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of Texas at AustinAustinUSA
  2. 2.Department of Computer ScienceUniversity of BremenBremenGermany

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