Physical bodies have an important role for agents to emerge complex behaviors to achieve the goal in dynamic real world, on which the traditional AI research has not paid so much attention. The RoboCup Physical Agent Challenge provides a good testbed to see how physical bodies play a significant role in realizing intelligent behaviors using RoboCup framework \cite{Kitano_et_al95}. RoboCup is an attempt to foster AI and Robotics research by using soccer game as a representative domain where wide-range of technologies can be integrated as well as new technologies can be developed. While the RoboCup envisions a set of longer range challenges over next few decades, it also encompasses various short term challenge goals. In this paper, we present three technical challenges as the RoboCup Physical Agent Challenge 97: (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 common side player (receiving, goal-keeping, and intercepting), and (3) passing the ball between two players. First two are concerned with single agent skills while the third one is related to a simple cooperative behavior. Why we set up these challenges and how we should evaluate the realized skills are given. The ultimate goal in AI, and probably in robotics, is to build intelligent systems capable of emerging complex behaviors to accomplish the given tasks through interactions with a dynamically changing physical world. The traditional AI research has been mainly seeking for the methodology of symbol manipulations to be used in knowledge acquisition and representation and reasoning on it with little attention on their applications in dynamic real worlds. While, in robotics much more emphasis have been put on the issues of designing and building hardware systems and their controls. However, recent topics spread over two areas include design principles of autonomous agents, multi-agent collaboration, strategy acquisition, real-time reasoning and planning, intelligent robotics, sensor-fusion, and behavior learning. These topics expose new aspects with which traditional approaches seem difficult to cope. In order to cope with these issues and finally to achieve the ultimate goal, physical bodies have an important role in enabling the system to interact with physical environments, that makes the system learn from the environment and develop its internal representation. The meanings of ``having a physical body'' can be summarized as follows: Sensing and acting capabilities are not separable, but tightly coupled. In order to accomplish the given tasks, the sensor and actuator spaces should be abstracted under the resource bounded conditions (memory, processing power, controller etc.). The abstraction depends on both the fundamental embodiments inside the agents and the experiences (interactions with their environments). The consequences of the abstraction are the agent-based subjective representation of the environment, and its evaluation can be done by the consequences of behaviors. Even though we should advocate the importance of ``having a physical body,'' it seems required to show how the system performs well coping with new issues in a concrete task domain. In other words, we need a standard problem which people regard as a new one that expose various kinds of important aspects of intelligent behaviors in real worlds.
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