RoboCup: The Robot World Cup Initiative

By H. Kitano, Minoru Asada, Yasuo Kuniyoshi, I. Noda, E. Osawa. 1997

The Robot World Cup Initiative (R, oboCup) is attempt to foster AI and intelligent rohoties research by providing a standard problem where wide range of technologies especially concerning multi-agent research (:an be integrated and examined. The first RoboCup competition is to be, heht at. IJCAI-97, Nagoya. In order for a robot team to actually perform a soccer game. various technologies must I)e incorl)orated including: design principles of autononmus agents, multi-agent collaboration, strategy acquisition, real-time rea.~oning, robotics, and sensor-fllsion. Unlike AAAI robot competition, which is tuned for a single heavy-duty slow-moving robot. RoboCup is a task for a team of multiple f‘ast-moving robots under a dynamic environmen(. Although RoboCnp’s final target is a worhl cup with real robots, RoboCup offers a soft.ware platform for reseaxch on the software aspects of RoboCup. This paper describes teclini(’M challenges involw~d in RoboCup, rules, and simulation environment.

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Citations in the corpus

TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations (2021, neutral): “Many researchers have developed agents for other football games (e.g., RoboCup Soccer Simulator (Kitano et al., 1997) and the DeepMind MuJoCo Multi-Agent Soccer Environment (Liu et al., 2019, 2021a)).”
Google Research Football: A Novel Reinforcement Learning Environment (2019, methodology): “There are other available football simulators, such as the RoboCup Soccer Simulator (Kitano et al. 1995; 1997), and the DeepMind MuJoCo Multi-Agent Soccer Environment (Liu et al. 2019).”
The StarCraft Multi-Agent Challenge (2019, background): “(2005) presented Keepaway soccer, a domain built on the RoboCup soccer simulator (Kitano et al., 1997), a 2D simulation of a football environment with simplified physics, where the main task consists of keeping a ball within a pre-defined area where agents in teams can reach, steal, and pass the bal”
Optimal and Approximate Q-value Functions for Decentralized POMDPs (2008, methodology): “2 we discussed the decentralized tiger (Dec-Tiger) problem as introduced by Nair et al. (2003b). Apart from the standard Dec-Tiger domain, we consider a modified version, called Skewed Dec-Tiger, in which the start distribution is not uniform.”
Emergent Coordination Through Competition (2019, background): “One longstanding challenge in AI has been robot soccer (Kitano et al., 1997), including simulated leagues, which has been tackled with machine learning techniques (Riedmiller et al., 2009; MacAlpine & Stone, 2018) but not yet mastered by endto-end reinforcement learning.”
A Framework to Describe, Analyze and Generate Interactive Motor Behaviors (2012, background): “While some studies have been made about robot-robot competition, such as the football Robocup [78], human-robot competitions are only planed, such as the football competition projected in 2050.”
Multi-objective multi-agent decision making: a utility-based analysis and survey (2019, background): “Examples of this setting include games with opposing teams of agents, such as RoboCup soccer [50].”
Three years of the RoboCup standard platform league drop-in player competition (2016, unknown): “RoboCup is an annual international robotics competition that had its first competition in 1997 [8].”
Half Field Offense: An Environment for Multiagent Learning and Ad Hoc Teamwork (2016, background): “RoboCup [13] is an international robot soccer competition that promotes research in AI and robotics.”
The RoboCup Synthetic Agent Challenge 97 (1997, background): “RoboCup (The World Cup Robot Soccer) is an attempt to promote AI and robotics research by providing a common task, Soccer, for evaluation of various theories, algorithms, and agent architectures [Kitano, et al., 1995; Kitano et al., 1997a; 1997b].”
Real-time randomized path planning for robot navigation (2002, background): “For RoboCup F180 robot control, the system for must take the input from a vision system, reporting the position of all field objects detected from a k e d overhead camera, and send the output to a radio server which sends velocity commands to the robots that are being controlled.”
Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement (2017, unknown): “Evaluation Scheme Description Robocup58 and FIRA59 Robotics (robot football/soccer) (Kitano et al, 1997; Kim, 2004).”
Towards collaborative and adversarial learning: a case study in robotic soccer (1998, background): “Although some low-level skills, such as dribbling, are entirely individual in nature, others, such as passing and receiving passes, are necessitated by the multiagent nature of the domain.”
Recent Research in Cooperative Control of Multivehicle Systems (2007, background): “RoboCup was initially conceived as an attempt to foster research in artificial intelligence, specifically that of multiple vehicles in a highly dynamic environment [25].”