RoboCup Humanoid League 2020: Lessons Learned From Going Virtual [Competitions]

RoboCup 2011 Humanoid League Winners

Over the past few years, soccer-playing humanoid robots advanced significantly. Elementary skills, such as bipedal walking, visual perception, and collision avoidance have matured enough to allow for dynamic and exciting games. In this paper, the three winning Humanoid League teams from the KidSize, TeenSize, and AdultSize class present their soccer systems. The KidSize winner team DARwIn used ...

RoboCup 2006 Humanoid League: UA Team Description

This paper describes the major design concerns, research approaches and scientific achievements of the UA-Team 1 humanoid robot. The project, started in 2003, has taken up the challenge to build a prototype capable of participating in the RoboCup Humanoid League, being the first time appearance of the team. The main features of the 22 degrees-of-freedom robot include the distributed control arc...

RoboCup 2013 Humanoid Kidsize League Winner

The RoboCup Humanoid Kidsize League has rocketed into an exceptionally competitive league, supported by recent introduction of the open source hardware and software platforms. Teams must improve substantially their robot teams every year to stay competitive. In addition to the important skills of walking speed, kick stability, and visual acuity, the 2013 season introduced rule changes to test r...

Multiagent Competitions and Research: Lessons from RoboCup and TAC

This paper compares and contrasts two recent series of competitions in which multiple agents compete directly against one another: the robot soccer world cup (RoboCup) and the trading agent competition (TAC). Both of these competitions have attracted large numbers of competitors and have motivated important research results in arti cial intelligence. Based on extensive personal experiences, bot...

Multiagent Competitions and Resear h: Lessons from RoboCup and TAC