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Abstract

Since its inception in 1997, RoboCup has developed into a truly unique and long-standing research community advancing robotics and artificial intelligence through various challenges, benchmarks, and test fields. The main purposes of this article are to evaluate the research and development achievements so far and to identify new challenges and related new research issues. Unlike other robot competitions and research conferences, RoboCup eliminates the boundaries between pure research activities and the development of full system designs with hardware and software implementations at a site open to the public. It also creates specific scientific and technological research and development challenges to be addressed. In this article, we provide an overview of RoboCup, including its league structure and related research issues. We also review recent studies across several research categories to show how participants (called RoboCuppers) address the research and development challenges before, during, and after the annual competitions. Among the diversity of research issues, we highlight two unique aspects of the challenges: the platform design of the robots and the game evaluations. Both of these aspects contribute to solving the research and development challenges of RoboCup and verifying the results from a common perspective (i.e., a more objective view). Finally, we provide concluding remarks and discuss future research directions.

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2020-05-03
2024-05-08
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Literature Cited

  1. 1. 
    Kitano H, Asada M, Kuniyoshi Y, Noda I, Osawa E 1995. RoboCup: the Robot World Cup Initiative. Proceedings of IJCAI-95 Workshop on Entertainment and AI/Alife https://www2.sonycsl.co.jp/person/kitano/RoboCup/RoboCup.ps
    [Google Scholar]
  2. 2. 
    Kitano H, Asada M, Kuniyoshi Y, Noda I, Osawa E, Matsubara H 1998. RoboCup: a challenge problem for AI and robotics. See Ref. 10 1–19
    [Google Scholar]
  3. 3. 
    Asada M, Kitano H, Noda I, Veloso M 1999. RoboCup: today and tomorrow—what we have learned. Artif. Intell. 110:193–214
    [Google Scholar]
  4. 4. 
    Stone P, Quinlan M, Hester T 2010. The essence of soccer, can robots play too. Soccer and Philosophy: Beautiful Thoughts on the Beautiful Game T Richards 75–88 Chicago: Open Court
    [Google Scholar]
  5. 5. 
    Nagatani K, Kiribayashi S, Okada Y, Otake K, Yoshida K, et al 2013. Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots. J. Field Robot. 30:44–63
    [Google Scholar]
  6. 6. 
    Nardi D, Noda I, Ribeiro F, Stone P, von Stryk O, Veloso M 2014. RoboCup soccer leagues. AI Mag. 35:377–85
    [Google Scholar]
  7. 7. 
    RoboCup. 2019. SSL-Vision wiki. GitHub https://github.com/RoboCup-SSL/ssl-vision/wiki
    [Google Scholar]
  8. 8. 
    Gerndt R, Seifert D, Baltes J, Sadeghnejad S, Behnke S 2015. Humanoid robots in soccer: robots versus humans in RoboCup 2050. IEEE Robot. Autom. Mag. 22:3147–54
    [Google Scholar]
  9. 9. 
    Noda I, Suzuki S, Matsubara H, Asada M, Kitano H 1998. Overview of RoboCup-97. See Ref. 10 20–41
    [Google Scholar]
  10. 10. 
    Kitano H 1998. RoboCup-97: Robot Soccer World Cup I Berlin: Springer
  11. 11. 
    Asada M, Kitano H 1999. RoboCup-98: Robot Soccer World Cup II Berlin: Springer
  12. 12. 
    Veloso M, Pagello E, Kitano H 2000. RoboCup-99: Robot Soccer World Cup III Berlin: Springer
  13. 13. 
    Stone P, Balch T, Kraetzschmar G 2001. RoboCup 2000: Robot Soccer World Cup IV Berlin: Springer
  14. 14. 
    Birk A, Coradeschi S, Tadokoro S 2002. RoboCup 2001: Robot Soccer World Cup V Berlin: Springer
  15. 15. 
    Kaminka G, Lima PU, Rojas R 2003. RoboCup 2002: Robot Soccer World Cup VI Berlin: Springer
  16. 16. 
    Bonarini A, Polani D, Browning B, Yoshida K 2004. RoboCup 2003: Robot Soccer World Cup VII Berlin: Springer
  17. 17. 
    Nardi D, Riedmiller M, Sammut C, Santos-Victor J 2005. RoboCup 2004: Robot Soccer World Cup VIII Berlin: Springer
  18. 18. 
    Bredenfeld A, Jacoff A, Noda I, Takahashi Y 2006. RoboCup 2005: Robot Soccer World Cup IX Berlin: Springer
  19. 19. 
    Lakemeyer G, Sklar E, Sorrenti DG, Takahashi T 2007. RoboCup 2006: Robot Soccer World Cup X Berlin: Springer
  20. 20. 
    Visser U, Ribeiro F, Ohashi T, Dellaert F 2008. RoboCup 2007: Robot Soccer World Cup XI Berlin: Springer
  21. 21. 
    Iocchi L, Matsubara H, Weitzenfeld A, Zhou C 2009. RoboCup 2008: Robot Soccer World Cup XII Berlin: Springer
  22. 22. 
    Baltes J, Lagoudakis MG, Naruse T, Ghidary SS 2010. RoboCup 2009: Robot Soccer World Cup XIII Berlin: Springer
  23. 23. 
    Ruiz-del-Solar J, Chown E, Ploger PG 2011. RoboCup 2010: Robot Soccer World Cup XIV Berlin: Springer
  24. 24. 
    Roefer T, Mayer N, Savage J, Saranl U 2012. RoboCup 2011: Robot Soccer World Cup XV Berlin: Springer
  25. 25. 
    Chen X, Stone P, Sucar L, van der Zant T 2013. RoboCup 2012: Robot Soccer World Cup XVI Berlin: Springer
  26. 26. 
    Behnke S, Veloso M, Visser A, Xiong R 2014. RoboCup 2013: Robot Soccer World Cup XVII Berlin: Springer
  27. 27. 
    Bianchi R, Akin H, Ramamoorthy S, Sugiura K 2015. RoboCup 2014: Robot Soccer World Cup XVIII Cham, Switz.: Springer
  28. 28. 
    Almeida L, Ji J, Steinbauer G, Luke S 2016. RoboCup 2015: Robot Soccer World Cup XIX Cham, Switz.: Springer
  29. 29. 
    Behnke S, Sheh R, Sarıel S, Lee DD 2017. RoboCup 2016: Robot Soccer World Cup XX Cham, Switz.: Springer
  30. 30. 
    Obst HAO, Sammut C, Tonidandel F 2018. RoboCup 2017: Robot Soccer World Cup XXI Cham, Switz.: Springer
  31. 31. 
    Holz D, Genter K, Saad M, von Stryk O 2019. RoboCup 2018: Robot Soccer World Cup XXII Cham, Switz.: Springer
  32. 32. 
    Suthakorn J, Williams M, Niemueller T, Chalu S 2020. RoboCup 2019: Robot Soccer World Cup XXIII Cham, Switz.: Springer
  33. 33. 
    Douven Y, Houtman W, Schoenmakers F, Koen Meessen HVDL, Bruijnen D 2019. Tech United Eindhoven Middle Size League winner 2018. See Ref. 31 413–24
    [Google Scholar]
  34. 34. 
    Junkai R, Chenggang X, Junhao X, Kaihong H, Huimin L 2016. A control system for active ball handling in the RoboCup Middle Size League. 2016 Chinese Control and Decision Conference4396–402 Piscataway, NJ: IEEE
    [Google Scholar]
  35. 35. 
    Beuermann M, Ossenkopf M, Geihs K 2020. Positioning of active wheels for optimal ball handling: a guide for designing a new ball handle mechanism for Middle-Size-League at RoboCup. See Ref. 32 30–43
    [Google Scholar]
  36. 36. 
    Gies V, Soriano T, Albert C, Prouteau N 2020. Modelling and optimisation of a RoboCup MSL coilgun. See Ref. 32 71–85
    [Google Scholar]
  37. 37. 
    Yoshimoto T, Horii T, Mizutani S, Iwauchi Y, Yamada Y 2017. OP-AmP 2017 team discription paper Descr. Pap., Asagami Works Osaka, Jpn: https://ssl.robocup.org/wp-content/uploads/2019/01/2017_TDP_Op-Amp.pdf
  38. 38. 
    Yoshimoto T, Horii T, Mizutani S, Iwauchi Y, Zenji S 2019. OP-AmP 2019 extended team discription paper Descr. Pap., Asagami Works Osaka, Jpn: https://ssl.robocup.org/wp-content/uploads/2019/03/2019_ETDP_OP-AmP.pdf
  39. 39. 
    Ha I, Tamura Y, Asama H, Han J, Hong DW 2011. Development of open humanoid platform DARwIn-OP. SICE Annual Conference 20112178–81 Piscataway, NJ: IEEE
    [Google Scholar]
  40. 40. 
    Fabre R, Rouxel Q, Passault G, N'Guyen S, Ly O 2017. Dynaban, an open-source alternative firmware for Dynamixel servo-motors. See Ref. 29 169–77
    [Google Scholar]
  41. 41. 
    Bestmann M, Guldenstein J, Zhang J 2020. High-frequency multi bus servo and sensor communication using the Dynamixel protocol. See Ref. 32 16–29
    [Google Scholar]
  42. 42. 
    Schwarz M, Pastrana J, Allgeuer P, Schreiber M, Schueller S 2014. Humanoid TeenSize open platform NimbRo-OP. See Ref. 26 568–75
    [Google Scholar]
  43. 43. 
    Farazi H, Ficht G, Allgeuer P, Pavlichenko D, Rodriguez D 2019. NimbRo robots winning RoboCup 2018 Humanoid AdultSize soccer competitions. See Ref. 31 436–49
    [Google Scholar]
  44. 44. 
    Yamamoto T, Takagi Y, Ochiai A, Iwamoto K, Itozawa Y 2020. Human support robot as research platform of domestic mobile manipulator. See Ref. 32 457–65
    [Google Scholar]
  45. 45. 
    Scheunemann MM, van Dijk SG 2020. ROS 2 for RoboCup. See Ref. 32 429–38
    [Google Scholar]
  46. 46. 
    Thielke F, Hasselbring A 2020. A JIT compiler for neural network inference. See Ref. 32 448–56
    [Google Scholar]
  47. 47. 
    Mitrevski A, Ploger PG 2020. Reusable specification of state machines for rapid robot functionality prototyping. See Ref. 32 408–17
    [Google Scholar]
  48. 48. 
    Mellmann H, Schlotter B, Strobel P 2020. Toward data driven development in RoboCup. See Ref. 32 176–88
    [Google Scholar]
  49. 49. 
    Berlin United Nao Team Humboldt. 2019. Tools for data driven research and development in RoboCup. Humboldt University Berlin https://www.naoteamhumboldt.de/en/projects/RoboCup-data-collection-and-evaluation
    [Google Scholar]
  50. 50. 
    Fiedler N, Bestmann M, Hendrich N 2019. ImageTagger: an open source online platform for collaborative image labeling. See Ref. 31 162–69
    [Google Scholar]
  51. 51. 
    Hess T, Mundt M, Weis T, Ramesh V 2018. Large-scale stochastic scene generation and semantic annotation for deep convolutional neural network training in the RoboCup SPL. See Ref. 30 33–44
    [Google Scholar]
  52. 52. 
    Visser A, Nardin LG, Castro S 2019. Integrating the latest artificial intelligence algorithms into the RoboCup rescue simulation framework. See Ref. 31 476–87
    [Google Scholar]
  53. 53. 
    van Dijk SG, Scheunemann MM 2019. Deep learning for semantic segmentation on minimal hardware. See Ref. 31 349–61
    [Google Scholar]
  54. 54. 
    Gholami A, Moradi M, Majidi M 2020. A simulation platform design and kinematics analysis of MRL-HSL humanoid robot. See Ref. 32 387–96
    [Google Scholar]
  55. 55. 
    Inamura T, Mizuchi Y 2018. Competition design to evaluate cognitive functions in human-robot interaction based on immersive VR. See Ref. 30 84–94
    [Google Scholar]
  56. 56. 
    Takami S, Takayanagi K, Jaishy S, Ito N, Iwata K 2018. Proposed environment to support development and experiment in RoboCup rescue simulation. See Ref. 30 7183
    [Google Scholar]
  57. 57. 
    MacAlpine P, Stone P 2017. UT Austin Villa RoboCup 3D simulation base code release. See Ref. 29 135–43
    [Google Scholar]
  58. 58. 
    Prokopenko M, Wang P 2020. Gliders2d: source code base for RoboCup 2D soccer simulation league. See Ref. 32 418–28
    [Google Scholar]
  59. 59. 
    Fiedler N, Brandt H, Gutsche J, Vahl F, Hagge J, Bestmann M 2020. An open source vision pipeline approach for RoboCup humanoid soccer. See Ref. 32 387–96
    [Google Scholar]
  60. 60. 
    Kohlbrecher S, Meyer J, von Stryk O, Klingauf U 2011. A flexible and scalable SLAM system with full 3D motion estimation. 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics155–60 Piscataway, NJ: IEEE
    [Google Scholar]
  61. 61. 
    Kohlbrecher S, Petersen K, Steinbauer G, Maurer J, Lepej P 2012. Community-driven development of standard software modules for search and rescue robots. 2010 IEEE International Symposium on Safety, Security, and Rescue Robotics Piscataway, NJ: IEEE https://doi.org/10.1109/SSRR.2012.6523917
    [Crossref] [Google Scholar]
  62. 62. 
    Google Code 2019. ssl-autonomous-refbox. Google Code https://code.google.com/p/ssl-autonomous-refbox
    [Google Scholar]
  63. 63. 
    Zhu D, Biswas J, Veloso M 2015. AutoRef: towards real-robot soccer complete automated refereeing. See Ref. 27 419–30
    [Google Scholar]
  64. 64. 
    Schoenmakers F, Koudijs G, Martinez CL, Briegel M, van Wesel H 2014. Tech United Eindhoven team description 2013: Middle Size League. Descr. Pap., Eindhoven Univ. Technol. Eindhoven, Neth: https://www.techunited.nl/media/files/TDP2013.pdf
    [Google Scholar]
  65. 65. 
    RoboCup-MSL. 2019. RefBox2015. GitHub https://github.com/RoboCup-MSL
    [Google Scholar]
  66. 66. 
    Huang Z, Chen L,, Li J, Wang Y, Chen Z 2019. RoboCup SSL 2018 champion team paper. See Ref. 31 401–12
    [Google Scholar]
  67. 67. 
    Speck D, Barros P, Weber C, Wermter S 2017. Ball localization for RoboCup soccer using convolutional neural networks. See Ref. 29 19–30
    [Google Scholar]
  68. 68. 
    Menashe J, Kelle J, Genter K, Hanna J, Liebman E 2018. Fast and precise black and white ball detection for RoboCup soccer. See Ref. 30 45–58
    [Google Scholar]
  69. 69. 
    Leiva F, Cruz N, Bugueño I, Ruiz-del-Solar J 2019. Playing soccer without colors in the SPL: a convolutional neural network approach. See Ref. 31 12234
    [Google Scholar]
  70. 70. 
    Teimouri M, Delavaran MH, Rezaei M 2020. A real-time ball detection approach using convolutional neural networks. See Ref. 32 323–36
    [Google Scholar]
  71. 71. 
    Kukleva A, Khan MA, Farazi H, Behnke S 2020. Utilizing temporal information in deep convolutional network for efficient soccer ball detection and tracking. See Ref. 32 112–25
    [Google Scholar]
  72. 72. 
    Felbinger GC, Göttsch P, Loth P, Peters L, Wege F 2019. Designing convolutional neural networks using a genetic approach for ball detection. See Ref. 31 150–61
    [Google Scholar]
  73. 73. 
    Cruz N, Lobos-Tsunekawa K, Ruiz-del-Solar J 2018. Using convolutional neural networks in robots with limited computational resources: detecting NAO robots while playing soccer. See Ref. 30 1930
    [Google Scholar]
  74. 74. 
    Houliston T, Chalup SK 2019. Visual mesh: real-time object detection using constant sample density. See Ref. 31 4556
    [Google Scholar]
  75. 75. 
    Szemenyei M, Estivill-Castro V 2019. Real-time scene understanding using deep neural networks for RoboCup SPL. See Ref. 31 96–108
    [Google Scholar]
  76. 76. 
    Szemenyei M, Estivill-Castro V 2020. ROBO: robust, fully neural object detection for robot soccer. See Ref. 32 309–22
    [Google Scholar]
  77. 77. 
    Poppinga B, Laue T 2020. JET-Net: real-time object detection for mobile robots. See Ref. 32 227–40
    [Google Scholar]
  78. 78. 
    Hagg A, Hegger F, Plöger PG 2017. On recognizing transparent objects in domestic environments using fusion of multiple sensor modalities. See Ref. 29 3–15
    [Google Scholar]
  79. 79. 
    Reyes E, Gomez C, Norambuena E, Ruiz-del-Solar J 2019. Near real-time object recognition for Pepper based on deep neural networks running on a backpack. See Ref. 31 287–98
    [Google Scholar]
  80. 80. 
    Loncomilla P, Ruiz-del-Solar J 2020. YoloSPoC: recognition of multiple object instances by using Yolo-based proposals and deep SPoC-based descriptors. See Ref. 32 154–65
    [Google Scholar]
  81. 81. 
    Houliston T, Metcalfe M, Chalup SK 2016. A fast method for adapting lookup tables applied to changes in lighting colour. See Ref. 28 190–201
    [Google Scholar]
  82. 82. 
    Gomez C, Mattamala M, Resink T, Ruiz-del-Solar J 2019. Visual SLAM-based localization and navigation for service robots: the Pepper case. See Ref. 31 32–44
    [Google Scholar]
  83. 83. 
    Schneider P, Memmesheimer R, Kramer I, Paulus D 2020. Gesture recognition in RGB videos using human body keypoints and dynamic time warping. See Ref. 32 281–93
    [Google Scholar]
  84. 84. 
    Kohl N, Stone P 2004. Policy gradient reinforcement learning for fast quadrupedal locomotion. IEEE International Conference on Robotics and Automation, 2004 32619–24 Piscataway, NJ: IEEE
    [Google Scholar]
  85. 85. 
    Kohl N, Stone P 2004. Machine learning for fast quadrupedal locomotion. Proceedings of the 19th National Conference on Artificial Intelligence611–16 Palo Alto, CA: AAAI Press
    [Google Scholar]
  86. 86. 
    Sugihara T, Nakamura Y, Inoue H 2002. Real-time humanoid motion generation through ZMP manipulation based on inverted pendulum control. Proceedings: 2002 IEEE International Conference on Robotics and Automation 21404–9 Piscataway, NJ: IEEE
    [Google Scholar]
  87. 87. 
    Hemker T, Sakamoto H, Stelzer M, von Stryk O 2009. Efficient walking speed optimization of a humanoid robot. Int. J. Robot. Res. 28:303–14
    [Google Scholar]
  88. 88. 
    Rodriguez D, Brandenburger A, Behnke S 2019. Combining simulations and real-robot experiments for Bayesian optimization of bipedal gait stabilization. See Ref. 31 70–82
    [Google Scholar]
  89. 89. 
    Zahn B, Fountain J, Houliston T, Biddulph A, Chalup S, Mendes A 2020. Optimization of robot movements using genetic algorithms and simulation. See Ref. 32 466–75
    [Google Scholar]
  90. 90. 
    Iverach-Brereton C, Baltes J, Postnikoff B, Carrier D, Anderson J 2016. Fuzzy logic control of a humanoid robot on unstable terrain. See Ref. 28 202–13
    [Google Scholar]
  91. 91. 
    Böckmann A, Laue T 2017. Kick motions for the NAO robot using dynamic movement primitives. See Ref. 29 3344
    [Google Scholar]
  92. 92. 
    Seekircher A, Visser U 2017. A closed-loop gait for humanoid robots combining LIPM with parameter optimization. See Ref. 29 71–83
    [Google Scholar]
  93. 93. 
    Masterjohn JG, Polceanu M, Jarrett J, Seekircher A, Buche C, Visser U 2016. Regression and mental models for decision making on robotic biped goalkeepers. See Ref. 28 177–89
    [Google Scholar]
  94. 94. 
    Lanari L, Urbann O, Hutchinson S, Schwarz I 2017. Boundedness approach to gait planning for the flexible linear inverted pendulum model. See Ref. 29 58–70
    [Google Scholar]
  95. 95. 
    Abdolmaleki A, Simões D, Lau N, Reis LP, Neumann G 2017. Learning a humanoid kick with controlled distance. See Ref. 29 45–57
    [Google Scholar]
  96. 96. 
    Kasaei M, Lau N, Pereira A 2020. A fast and stable omnidirectional walking engine for the Nao humanoid robot. See Ref. 32 99–111
    [Google Scholar]
  97. 97. 
    Peña P, Visser U 2020. Adaptive walk-kick on a bipedal robot. See Ref. 32 213–26
    [Google Scholar]
  98. 98. 
    Mitrevski A, Padalkar A, Nguyen M, Ploger PG 2020. “Lucy, take the noodle box!”: domestic object manipulation using movement primitives and whole body motion. See Ref. 32 189–200
    [Google Scholar]
  99. 99. 
    Renault B, Saraydaryan J, Simonin O 2020. Towards S-NAMO: socially-aware navigation among movable obstacles. See Ref. 32 41–54
    [Google Scholar]
  100. 100. 
    Makarov PA, Yirtici T, Akkaya N, Aytac E, Say G 2020. A model-free algorithm of moving ball interception by holonomic robot using geometric approach. See Ref. 32 166–75
    [Google Scholar]
  101. 101. 
    Ommer N, Stumpf A, von Stryk O 2018. Real-time online adaptive feedforward velocity control for unmanned ground vehicles. See Ref. 30 3–16
    [Google Scholar]
  102. 102. 
    Balaban D, Fischer A, Biswas J 2017. A real-time solver for time-optimal control of omnidirectional robots with bounded acceleration. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems8027–32 Piscataway, NJ: IEEE
    [Google Scholar]
  103. 103. 
    Lobos-Tsunekawa K, Leottau DL, Ruiz-del-Solar J 2018. Toward real-time decentralized reinforcement learning using finite support basis functions. See Ref. 30 95–107
    [Google Scholar]
  104. 104. 
    Watkinson WB, Camp T 2019. Training a RoboCup striker agent via transferred reinforcement learning. See Ref. 31 109–21
    [Google Scholar]
  105. 105. 
    Asada M, Noda S, Tawaratumida S, Hosoda K 1996. Purposive behavior acquisition for a real robot by vision-based reinforcement learning. Mach. Learn. 23:279–303
    [Google Scholar]
  106. 106. 
    Abreu M, Reis LP, Lau N 2020. Learning to run faster in a humanoid robot soccer environment through reinforcement learning. See Ref. 32 3–15
    [Google Scholar]
  107. 107. 
    Leiva F, Lobos-Tsunekawa K, Ruiz-del-Solar J 2020. Collision avoidance for indoor service robots through multimodal deep reinforcement learning. See Ref. 32 140–53
    [Google Scholar]
  108. 108. 
    Wiley T, Bratko I, Sammut C 2018. A machine learning system for controlling a rescue robot. See Ref. 30 108–19
    [Google Scholar]
  109. 109. 
    Rizzi C, Johnson CG, Vargas PA 2018. Fear learning for flexible decision making in RoboCup: a discussion. See Ref. 30 59–70
    [Google Scholar]
  110. 110. 
    Simoes D, Lau N, Reis LP 2019. Adjusted bounded weighted policy learner. See Ref. 31 32436
    [Google Scholar]
  111. 111. 
    Holtz J, Guha A, Biswas J 2018. Interactive robot transition repair with SMT. Proceedings of the 27th International Joint Conference on Artificial Intelligence4905–11 Palo Alto, CA: AAAI Press
    [Google Scholar]
  112. 112. 
    Celemin C, Ruiz-del-Solar J 2016. Interactive learning of continuous actions from corrective advice communicated by humans. See Ref. 28 16–27
    [Google Scholar]
  113. 113. 
    Grupp M, Kopp P, Huber P, Rätsch M 2017. A 3D face modelling approach for pose-invariant face recognition in a human-robot environment. See Ref. 29 121–34
    [Google Scholar]
  114. 114. 
    Saraydaryan J, Leber R, Jumel F 2020. People management framework using a 2D camera for human-robot social interactions. See Ref. 32 268–80
    [Google Scholar]
  115. 115. 
    Yamaguchi M, Iwamoto G, Abe Y, Tanaka Y, Ishida Y 2019. Live demonstration: a VLSI implementation of time-domain analog weighted-sum calculation model for intelligent processing on robots. 2019 IEEE International Symposium on Circuits and Systems Piscataway, NJ: IEEE https://doi.org/10.1109/ISCAS.2019.8702222
    [Crossref] [Google Scholar]
  116. 116. 
    Tanaka Y, Tamukoh H 2019. Hardware implementation of brain-inspired amygdala model. 2019 IEEE International Symposium on Circuits and Systems Piscataway, NJ: IEEE https://doi.org/10.1109/ISCAS.2019.8702430
    [Crossref] [Google Scholar]
  117. 117. 
    Lu D, Chen X 2016. Towards an architecture combining grounding and planning for human-robot interaction. See Ref. 28 214–25
    [Google Scholar]
  118. 118. 
    Gemignani G, Veloso M, Nardi D 2016. Language-based sensing descriptors for robot object grounding. See Ref. 28 3–15
    [Google Scholar]
  119. 119. 
    Matamoros M, Harbusch K, Paulus D 2019. From commands to goal-based dialogs: a roadmap to achieve natural language interaction in RoboCup@Home. See Ref. 31 217–29
    [Google Scholar]
  120. 120. 
    Walker N, Peng YT, Cakmak M 2020. Neural semantic parsing with anonymization for command understanding in general-purpose service robots. See Ref. 32 337–50
    [Google Scholar]
  121. 121. 
    Jumel F, Saraydaryan J, Leber R, Matignon L, Lombardi E 2019. Context aware robot architecture, application to the RoboCup@Home challenge. See Ref. 31 205–16
    [Google Scholar]
  122. 122. 
    Peña P, Polceanu M, Lisetti C, Visser U 2019. eEVA as a real-time multimodal agent human-robot interface. See Ref. 31 262–74
    [Google Scholar]
  123. 123. 
    Fukushima T, Nakashima T, Akiyama H 2019. Mimicking an expert team through the learning of evaluation functions from action sequences. See Ref. 31 170–80
    [Google Scholar]
  124. 124. 
    Gabel T, Kloppner P, Godehardt E, Tharwat A 2019. Communication in soccer simulation: on the use of wiretapping opponent teams. See Ref. 31 3–15
    [Google Scholar]
  125. 125. 
    Cooksey P, Mendoza JP, Veloso M 2017. Opponent-aware ball-manipulation skills for an autonomous soccer robot. See Ref. 29 84–96
    [Google Scholar]
  126. 126. 
    Adachi Y, Ito M, Naruse T 2017. Classifying the strategies of an opponent team based on a sequence of actions in the RoboCup SSL. See Ref. 29 109–20
    [Google Scholar]
  127. 127. 
    Schwab D, Zhu Y, Veloso M 2019. Learning skills for Small Size League RoboCup. See Ref. 31 83–95
    [Google Scholar]
  128. 128. 
    Laureano MAP, Tonidandel F 2020. Analysis of the PSO parameters for a robots positioning system in SSL. See Ref. 32 126–39
    [Google Scholar]
  129. 129. 
    Dias R, Cunha B, Azevedo JL, Pereira A, Lau N 2019. Multi-robot fast-paced coordination with leader election. See Ref. 31 19–31
    [Google Scholar]
  130. 130. 
    Dias R, Amaral F, Angelico I, Azevedo JL, Cunha B 2019. CAMBADA'2019: team description paper Descr. Pap., CAMBADA, Univ. Aveiro Aveiro, Port: https://tdp.robocup.org/tdp/2019-tdp-cambada-robocupsoccer-middle-size
  131. 131. 
    Houtman W, Kengan C, van Lith P, ten Berge R, Haverlag M 2019. Tech United Eindhoven team description 2019 Descr. Pap., Tech United Eindhoven, Eindhoven Univ. Technol Eindhoven, Neth: https://tdp.robocup.org/tdp/2019-tdp-tech-united-eindhoven-robocupsoccer-middle-size
  132. 132. 
    Wang X, Zhao Y, Chen S, Liu X, Zhang W 2019. Water team description 2019 Descr. Pap., Water, Beijing Inf. Sci. Technol. Univ Beijing: https://tdp.robocup.org/tdp/2019-tdp-water-robocupsoccer-middle-size
  133. 133. 
    Yao W, Luo S, Lu H, Xiao J 2019. Distributed circumnavigation control with dynamic spacing for a heterogeneous multi-robot system. See Ref. 31 374–86
    [Google Scholar]
  134. 134. 
    Wong AS, Jeffery R, Turner P, Sleap S, Chalup SK 2019. RoboCup Junior in the Hunter region: driving the future of robotic STEM education. See Ref. 31 362–73
    [Google Scholar]
  135. 135. 
    Hughes J, Shimizu M, Visser A 2020. A review of robot rescue simulation platforms for robotics education. See Ref. 32 86–98
    [Google Scholar]
  136. 136. 
    Zug S, Niemueller T, Hochgeschwender N, Seidensticker K, Seidel M 2017. An integration challenge to bridge the gap among industry-inspired RoboCup leagues. See Ref. 29 157–68
    [Google Scholar]
  137. 137. 
    Gerndt R, Paetzel M, Baltes J, Ly O 2019. Bridging the gap - on a Humanoid Robotics Rookie League. See Ref. 31 193–204
    [Google Scholar]
  138. 138. 
    Pavez M, Ruiz-del-Solar J, Amo V, zu Driehausen FM 2019. Towards long-term memory for social robots: proposing a new challenge for the RoboCup@Home league. See Ref. 31 251–61
    [Google Scholar]
  139. 139. 
    Shimizu M, Takahashi T 2019. Survey of rescue competitions and proposal of new standard task from ordinary tasks. See Ref. 31 311–23
    [Google Scholar]
  140. 140. 
    Gabel T, Falkenberg E, Godehardt E 2017. Progress in RoboCup revisited: the state of soccer simulation 2D. See Ref. 29 144–56
    [Google Scholar]
  141. 141. 
    Michael O, Obst O, Schmidsberger F, Stolzenburg F 2018. Analysing soccer games with clustering and conceptors. See Ref. 30 120–31
    [Google Scholar]
  142. 142. 
    Suzuki Y, Nakashima T 2020. On the use of simulated future information for evaluating game situations. See Ref. 32 294–308
    [Google Scholar]
  143. 143. 
    Michael O, Obst O, Schmidsberger F, Stolzenburg F 2019. RoboCupSimData: software and data for machine learning from RoboCup Simulation League. See Ref. 31 230–37
    [Google Scholar]
  144. 144. 
    Pomas T, Nakashima T 2019. Evaluation of situations in RoboCup 2D simulations using soccer field images. See Ref. 31 275–86
    [Google Scholar]
  145. 145. 
    Suzuki Y, Fukushima T, Thibout L, Nakashima T, Akiyama H 2020. Game-watching should be more entertaining: real-time application of field-situation prediction to a soccer monitor. See Ref. 32 439–47
    [Google Scholar]
  146. 146. 
    Fukushima T, Nakashima T, Akiyama H 2020. Similarity analysis of action trajectories based on kick distributions. See Ref. 32 58–70
    [Google Scholar]
  147. 147. 
    Gabel A, Heuer T, Schiering I, Gerndt R 2019. Jetson, where is the ball? Using neural networks for ball detection at RoboCup 2017. See Ref. 31 181–92
    [Google Scholar]
  148. 148. 
    Speck D, Bestmann M, Barros P 2019. Towards real-time ball localization using CNNs. See Ref. 31 337–48
    [Google Scholar]
  149. 149. 
    Massouh N, Brigato L, Iocchi L 2020. RoboCup@Home-Objects: benchmarking object recognition for home robots. See Ref. 32 397–407
    [Google Scholar]
  150. 150. 
    Kramer ER, Sainz AO, Mitrevski A, Ploger PG 2020. Tell your robot what to do: evaluation of natural language models for robot command processing. See Ref. 32 255–67
    [Google Scholar]
  151. 151. 
    Niemueller T, Reuter S, Ferrein A, Jeschke S, Lakemeyer G 2016. Evaluation of the RoboCup Logistics League and derived criteria for future competitions. See Ref. 28 31–43
    [Google Scholar]
  152. 152. 
    Asada M, Hosoda K, Kuniyoshi Y, Ishiguro H, Inui T, et al 2009. Cognitive developmental robotics: a survey. IEEE Trans. Auton. Ment. Dev. 1:12–34
    [Google Scholar]
  153. 153. 
    Verschure PF 2012. Distributed adaptive control: a theory of the mind, brain, body nexus. Biol. Inspired Cogn. Architect. 1:55–72
    [Google Scholar]
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