Sho'ji Suzuki

The robocup physical agent challenge: Phase i

Traditional AI research has not given due attention to the important role that physical bodies play for agents as their interactions produce complex emergent behaviors to achieve goals in the dynamic real world. The RoboCup Physical Agent Challenge provides a good test bed for studying how physical bodies play a significant role in realizing intelligent behaviors using the RoboCup framework (Kitano et al., 1995). In order for the robots to play a soccer game reasonably well, a wide range of technologies needs to be integrated, and a number of technical breakthroughs must be made. In this article, we present three challenging tasks as the RoboCup Physical Agent Challenge Phase I: (1) moving the ball to the specified area (shooting, passing, and dribbling) with no, with stationary, or with moving obstacles; (2) catching the ball from an opponent or a teammate (receiving, goal keeping, and intercepting); and (3) passing the ball between two players. The first two tasks are concerned with single-agent skills,...

RoboCup-97: The First Robot World Cup Soccer Games and Conferences

RoboCup-97, The First Robot World Cup Soccer Games and Conferences, was held at the Fifteenth International Joint Conference on Artificial Intelligence. There were two leagues: (1) real robot and (2) simulation. Ten teams participated in the real-robot league and 29 teams in the simulation league. Over 150 researchers attended the technical workshop. The world champions are CMUNITED (Carnegie Mellon University) for the small-size league, DREAMTEAM (University of Southern California) and TRACKIES (Osaka University, Japan) for the middle-size league, and AT-HUMBOLDT (Humboldt University) for the simulation league. The Scientific Challenge Award was given to Sean Luke (University of Maryland) for his genetic programming- based simulation team LUKE, and the Engineering Challenge Awards were given to UTTORI UNITED (Utsunomiya University, Toyo University, and Riken, Japan) and RMIT (Royal Melbourne Institute of Technology, Australia) for designing novel omnidirectional driving mechanisms. Over 5000 spectators and 70 international media covered the competition worldwide. RoboCup-98, the Second Robot World Cup Soccer, was held in conjunction with the Third International Conference on Multiagent Systems in Paris, France, in July 1998.

An infra-red sensory system with local communication for cooperative multiple mobile robots

Distributed autonomous robotic systems which are composed of multiple robotic agents have been attracted the attention of many researchers as a new strategy for flexible and robust robotic systems. For cooperative multiple mobile robots in distributed autonomous robotic systems, recognition of the dynamic environment is required. In this paper, a new sensory system with local communication functionality is developed utilizing infra-red devices. Based on the discussion on sensing and communication for cooperative multiple mobile robots, requirements for the sensory system are derived. Then, the infra-red sensory system is designed and developed taking account of the required function. This system enables not only detection of collisions against obstacles or other robots but also local communication between robots. This system can also detect interference of plural infra-red signals. Finally the developed system as introduced, and its performance obtained as a results of experiments is shown.

Collision avoidance among multiple autonomous mobile robots using LOCISS (locally communicable infrared sensory system)

Collision avoidance is an essential problem for applications of multiple mobile robots. The authors have proposed new sensor system called LOCISS (Locally Communicable Infrared Sensory System) to detect robots and obstacles using infrared local communication. Robots mounting LOCISS can exchange useful information for collision avoidance such as speed and moving direction. In this paper, a set of rules for collision avoidance among multiple autonomous mobile robots using LOCISS is proposed. Each robot mutually carries out collision avoidance using the proposed rule. The validity of this set of rules is shown through a series of experiments using mobile robots.

The RoboCup Physical Agent Challenge: Goals and Protocols for Phase 1

Traditional AI research has not given due attention to the important role that physical bodies play for agents as their interactions produce complex emergent behaviors to achieve goals in the dynamic real world. The RoboCup Physical Agent Challenge provides a good test-bed for studying how physical bodies play a significant role in realizing intelligent behaviors using the RoboCup framework [Kitano, et al., 95]. In order for the robots to play a soccer game reasonably well, a wide range of technologies needs to be integrated and a number of technical breakthroughs must be made. In this paper, we present three challenging tasks as the RoboCup Physical Agent Challenge Phase I: (1) moving the ball to the specified area (shooting, passing, and dribbling) with no, stationary, or moving obstacles, (2) catching the ball from an opponent or a teammate (receiving, goal-keeping, and intercepting), and (3) passing the ball between two players. The first two are concerned with single agent skills while the third one is related to a simple cooperative behavior. Motivation for these challenges and evaluation methodology are given.

RoboCup Jr.: RoboCup for edutainment

The Robot World Cup Initiative (RoboCup) is an international joint project to promote science and technology in intelligent robotics, AI and related fields with the specific goal of building a fully humanoid robot that can be the human world cup champion by 2050, and to apply technologies developed in this activity to socially significant issues such as rescue, and for next generation industries. This idea inspired the imagination of people and it has grown to be one of the largest research areas in multi-agent robotics. The paper discusses extension of RoboCup activity for education and edutainment. Specifically, RoboCup has initiated a new project called RoboCup Jr. that aims at promotion of engineering education and popularization of robotics using RoboCup. A RoboCup Jr. league will be created to meet diverse needs of the education field, and a series of organizational frameworks are proposed to develop a network of social, personal, educational, and technical infrastructures to assist those who are interested in RoboCup Jr. activity.

What we learned from RoboCup-97 and RoboCup-98

RoboCup is an increasingly successful attempt to promote the full integration of robotics and AI research. The most prominent feature of RoboCup is that it provides the researchers with the opportunity to demonstrate their research results as a form of competition in a dynamically changing hostile environment, defined as the international standard game definition, in which the gamut of intelligent robotics research issues are naturally involved. The article describes what we have learned from the past RoboCup activities, and overview the future perspectives of RoboCup in the next century, mainly focusing on the real robot leagues. Finally, we introduce the new leagues, one of which will have been held at RoboCup-99 in Stockholm.

Vision-Based Robot Learning Towards RoboCup: Osaka University "Trackies"

The authors have applied reinforcement learning methods to real robot tasks in several aspects. We selected a skill of soccer as a task for a vision-based mobile robot. In this paper, we explain two of our method; (1)learning a shooting behavior, and (2)learning a shooting with avoiding an opponent. These behaviors were obtained by a robot in simulation and tested in a real environment in RoboCup-97. We discuss current limitations and future work along with the results of RoboCup-97.

TRACKIES: RoboCup-97 Middle-Size League World Cochampion

This article describes a milestone in our research efforts toward the real robot competition in RoboCup. We participated in the middle-size league at RoboCup-97, held in conjunction with the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. The most significant features of our team, TRACKIES, are the application of a reinforcement learning method enhanced for real robot applications and the use of an omnidirectional vision system for our goalie that can capture a 360-degree view at any instant in time. The method and the system used are shown with competition results.