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I.Ball Moving Objectives The objectives of this challenge is to check how the most fundamental skill of moving a ball to the specified area under several conditions with no (Level I), stationary (Level II), or moving (Level III) obstacles in the field can be acquired in various kinds of agent architectures, and to evaluate merits and demerits of realized skills using the standard tasks. The specifications of the ball and the surface of the field is the common issue to the all challenges in the physical agent track. In order to emerge various behaviors, the field surface should not so rough as preventing the ball from rolling, but not so smooth as no friction. The former means no kicks or passes, and the latter no dribbles. Since the variations of the task environment is few in Level I, agent architecture, specially sensing capability is focused. While, in Level II motion control is the central issue, and in Level III prediction of the motion of obstacles is the key issue. Technical Issues Vision General computer and robot vision issues are too broad to deal with here. Finding and tracking independently moving objects (ball, players, judges) and estimating their motion parameters (2-D and further 3-D) from complicated background (field lines, goals, corner poles, flags waved by the supporters in the stadium) is too difficult for the current computer and robot vision technology to completely perform in realtime. In order to focus on the skill acquisition, visual image processing should be drastically simplified. Discrimination by color information such as a red ball, a blue goal, a yellow opponent makes it easy to find and track objects in realtime \cite{Asada96a}. Nevertheless, robust color discrimination is a tough problem because digitized signals are so naive against the slight changes of lighting conditions. In the case of remote (wireless) processing, much more noises due to environmental factors cause fatal errors in image processing. Currently, human programmer adjusts key parameters used in discriminating colored objects on site. Self calibration method should be developed, which can expand image processing applications much more widely in general. In the other aspect, visual tracking hardware based on image intensity correlation inside window region can be used to find and track objects from the complicated background by setting the initial windows \cite{Inoue92}. Currently, color tracking version is commercially available. As long as the initialized color pattern inside each window does not change so much, tracking is almost successful. Coping with pattern changes due to lighting conditions and occlusions is one of the central issues in the case of using this sort of hardware system \cite{Adachi96b}. As long as the vision system can cope with the above issues, and capture the images of both the specified area (the target) and the ball, there might be no problem \cite{Nakamura95d,Nakamura96a}. To prevent the agent from losing the target, and/or the ball (in Level II and III, obstacles, too), an active vision system with panning and tilting motions seems preferable, but this makes the control system more complicated and causes spatial memory organization problem for keeping the information of lost objects. More practical way is to use a wider-angle lens. One extreme of this sort is to use the omni directional vision system to capture the image all around the agent. This sort of lens seems very useful not only acquiring the basic skills but also realizing cooperative behaviors in multi agent environment. Currently this type of lens is commercially available as conic and hyperboloidal ones \cite{Ishiguro96}. Other perception In the case of other sensing strategies, the agent should find the ball, (in Level II and III, obstacles, too) and know what is the target. Beside vision, typical sensors used in mobile robot research are range finder, sonar, and bumper ones. However, it seems difficult for each or any combination among them to discriminate the ball (obstacles, too in higher levels) and the target unless a special equipment such as transmitter is set inside the ball or the target, or a global positioning system besides on-board sensing and communication lines are used to inform the positions of all agents. The simplest case is no on-board sensing but only a global positioning system, which is adopted in the small robot league in the physical agent track because on-board sensing facilities are limited due to its size regulation. In Level II and III, obstacle avoidance behavior and coordination between it and ball carrying (or passing/shooting) behavior are required. One good strategy is assign the sensor roles in advance. For example, sonar and bumper sensors are used for obstacle avoidance while vision sensor is used for the target reaching. One can make the robot learn to assign the sensor roles \cite{Nakamura96c}. Action As described in section \ref{sec:RI}, total balance of the whole system is a key issue to design the robot. In order for the system to expose more various kinds of behaviors, more complicated mechanical system and its sophisticated control techniques are necessary. We should start with a simpler one and then step up. The simplest case is to use just a car-like vehicle which has only two DOFs (degrees of freedom, in this case forward and turn), and pushes the ball to the target (dribbling). The target can be just a location, the goal (shooting), and one of common side players (passing). In the case of location, a dribbling skill to carry the ball to the location might be sufficient. In the case of latters, the task is to kick the ball into the desired direction without caring about final position of the ball. To discriminate it from a simple dribbling skill, we may need more DOFs to realize a kick motion with one feet (or we may call arm). In the case of passing, velocity control of the ball might be a technical issue because one of common players to be passed is not stationary but moving. In Level II and III, obstacle avoidance behavior and coordination between it and ball carrying (or passing/shooting) behavior are required. To smoothly switch two behaviors, the robot should speed down, but this increase the possibility to be gotten the ball by the opponent. To avoid these situations, the robot quickly switch the behaviors, which causes unstability of the robot motion. One can use the omni-directionally movable vehicle based on the sophisticated mechanism \cite{Asama95}. The vehicle can move to any direction anytime. In addition to the motion control problem, there are more issues to be considered such as how to coordinate these two behaviors (switching conditions) \cite{Uchibe96c}. Mapping from perception to action There are several approaches to implement the control mechanisms which perform the given task. A conventional approach is first to reconstruct the geometrical model of the environment (ball, goal, other agents etc.), then deliberate a plan, and finally execute the plan. However, this sort of approach is not suitable for dynamically changing game environment due to its time-consuming reconstruction process although a simple carrying task (Level I) can be performed. A look up table indicating the mapping from perception to action by whatever method seems suitable for quick action selection. One can make such an LUT by hand-coding given a priori, precise knowledge of the environment (the ball, the goals, and other agents) and the agent model (kinematics/dynamics). In a simple task domain, human programmer can do that to some extent, but seems difficult to cope with possible situations completely. An opposite approach is learning to decide action selection given almost no a priori knowledge. Between them, several variations with more or less knowledge. The approaches are summarized as follows: \begin{enumerate} \item complete hand-coding (no learning), \item parameter tuning given the structural (qualitative) knowledge (self calibration), \item typical reinforcement learning such as Q-learning with almost no a priori knowledge, but given the state and action spaces \cite{Asada96a,Uchibe96c}, \item action selection from the state and action space construction \cite{Asada96h,Takahashi96b}, \item tabula rasa learning (nothing assumed?) \end{enumerate} These approaches should be evaluated in various kinds of viewpoints. Evaluation In order to evaluate the achieved skills, we set up the following standard tasks with some variations.
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