CONCLUSIONES DEL ESTUDIO CONCLUYENTE

The RoboCup initiative was started in 1997 with the paper (Kitano et al. 1997). The idea was to foster AI and robotics research with proposing a common application domain: soccer. They provided the vision

“By the year 2050, [to] develop a team of fully autonomous humanoid robots that can win against the human soccer world champion team.”

(RoboCup 2006) While this is clearly an ambitious goal, interesting insights about how to design and control au- tonomous humanoid robots can be made on the way towards this goal.

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Several questions arise regarding the vision behind the RoboCup initiative. Why does one need a team of humanoid robots playing soccer? Why is it important to have a common test bed for research? Why soccer?

Soccer is of special interest as a common test bed because it has a very interesting characteris- tic: it is a cooperative and adversarial multi-agent/robot domain. There is a common goal (to win the game) which can only be achieved in a team, cooperatively. There are opponents that try to foil the own endeavors. We will discuss the domain aspects in detail below.

Even if one is not convinced that one needs a team of humanoid soccer playing robots the RoboCup idea seems to push the development towards the right direction. From a robotics per- spective the interesting fields are how to build and control those robots or agents, including re- search on human gait and running, mobile power supply, control theory, engineering. From an AI perspective interesting fields are foundations of cooperative multi-agent systems, behavior pro- gramming, strategy acquisition, or decision making.

Trying to solve all problems at once will probably not lead to success. Therefore, RoboCup is organized in several different leagues which all concentrate on sub-problems.

Simulation League. The Simulation league, one of the first leagues in RoboCup, concentrates on agent research. It is, as the name says, a simulated league, where two teams of eleven software agent compete in a simulated environment. A simulation server, the Soccerserver (Noda et al. 1997), which receives the action commands from the agents and dispatches the sensory informa- tion to each agent exists. The Soccerserver calculates the visible information for each player and sends it in form of a string message to each agent via an UDP socket. The information of the visi- ble landmarks of an agent are computed in an egocentric view. Besides the visual information the soccer server also sends aural messages to the player, i.e. player can shout and in a close distance around this can be heard by other players. The amount of data which can be sent via these “say” messages is restricted to 10 bytes per simulation cycle. From this information each agent has to construct a world model. Agents can settle actions by sending one of five basic actions back to the server. These actions are dash, kick, turn, catch (for the goal keeper only), tackle. The Soc- cerserver controls also the game play. An automated referee judges offsides, throw-ins and counts the goals. The simulation takes place in simulation cycles of 100 ms. That means that each agent can send an action to the Soccerserver every 100 ms. Visible and audible information are sent to the players every 150 ms. For the last three years a new simulation environment was established which extends the simulation to 3D (Obst and Rollmann 2004).

Small-size League. The Small-size league is a robotic league. Five small wheeled robots play on a field of the size of a table tennis board with a golf ball. As the robots are too small to carry sensors on-board, a ceiling camera is installed above the field. The camera images are sent to each team. Vision processing extracts the relevant information from the images. To alleviate the recognition, each player has a special color coding on top. With these information the actions the

robots should perform are calculated by a computer off the field. The actions are sent back via radio to the robots. Thus, the league is partly autonomous. The research focus here is mainly on image processing and decision making.

Middle-size League. Two teams of up to five fully autonomous wheeled robots compete on a field of the size_{8 × 12 m.}3 _{The robots may have a maximal size of50 × 50 cm and the height}

may not exceed80 cm. Like in the other soccer leagues are the goals and the ball color-coded to ease perception. The goals are painted blue and yellow, the ball is orange. The research focus of the Middle-size league is on robotics and on decision making. It turns out especially in this league that the whole system, hardware as well as the software, must form a unit. Only well integrated overall systems are competitive.

Four-legged League. While in the Small-size and the Middle-size league the hardware is developed by the participating teams and part of the research, the Four-legged league aims at developing robot control software on a restricted but common platform. The robots here are Aibo dog robots from Sony. The different developments and achievements made by the teams can be well compared, as they all work on the same platform. The robot’s capabilities are limited. It has only a very small camera resolution, the sensor values of the joints are very noisy. Another problem in this league regarding the hardware platform is that Sony does not provide enough information about the hardware such that several controllers had the be reverse-engineered in order to lea rn how they work. Another remarkable note in this league is that it allows for distributed development. For example, there is a German Team (R ¨ofer et al. 2004) where five universities work successfully together on the same code. Clearly, this is supported by the common hardware. Unfortunately, this league will sooner or later come to an end as Sony stopped the production of the Aibo in 2006.

Humanoid League. The ultimate goal of the RoboCup initiative is to play (robotic) soccer with humanoid robots. Of course, research on human-like robots must be conducted in order to achieve this goal. The humanoid league has existed for three years and makes remarkable progress. In the beginning, the competitions were only so-called technical challenges, where the teams showed the capabilities of their robots. Today, there are already soccer matches two-on-two. There are two different sub-leagues based on the sizes of robots, the so-called Kid-size league and the Teen- size league. In the Kid-size league, robots with a height from30 cm to 60 cm compete, while a typical Teen-size robot measures between65 cm and 130 cm, although in special cases robots up to180 cm may participate in this league.

Rescue Leagues. Besides the soccer activities RoboCup has a broader scope. It turned out that the general idea to have competitions to foster certain research fields and aspects works very well. The Rescue leagues aim at rescuing entombed people from urban disaster areas, both in simulation

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and with real robots. In the simulated leagues, research is about strategic planning, how resources like fire trucks has to be scheduled. In the hardware league, the partly autonomous robots must detect people. The problem is, for one, to detect the victims by the sensors, and for another that the robots have to maneuver in rough terrain.

RoboCup@Home League. The RoboCup@Home league was established in 2006. The idea here is to foster service robotics research. In a human apartment environment the robots should perform service robotics tasks. In the first competition the tasks were to safely maneuver through the apartment, follow a person, and pick up a newspaper. Further some free performance was allowed to show the capabilities of the robot. The focus of the several tests are the general applica- bility of the methods. Thus, each team has only five minutes time to adapt the environmental map of the robot to changes. Only the floor plan may be mapped in advance. The tests are conducted in such a way that first the developer may present the test to show the general applicability. In a second run the robot has to fulfill the task with a referee instructing the robot. An important aspect of the RoboCup@Home league is human machine interaction. Among other things, the robots should be able to understand natural language commands.

RoboCup Junior. Another important aspect of RoboCup is to interest students below university level for robotics. In the RoboCup Junior league students at high-school level program Lego Mindstorm robots for dance competitions or to fulfill simple tasks like follow a line on the floor.

A key aspect for research and education is the competition idea. In annual competitions re- searchers and students from all over the world get together. Participants can easily exchange experiences or research ideas. As the open source idea is widely spread with the RoboCup teams, it also possible not only to exchange ideas but also code. This helps a lot to bring the develop- ment further. Next, we will concentrate on the question why the soccer domain is an interesting research domain. In addition to the domain aspects discussed in Russel and Norvig (2003) we will concentrate on several other interesting properties of the soccer domain in the following and their impact to the system design of robots or agents acting in these domains.

The dynamic and real-time aspect of a domain means for one that the environment changes at any time unlike checkers which is round-based, for another that decision and actions must be taken in real-time. The decision cycles are short. The longer it takes to come to a decision the more the performance of the system decreases. The real-time aspect refers to nearly all system aspects. On the highest level this means that the decision which action is to be performed next has to be taken quickly. Consider the soccer domain where a player is in ball possession. If it takes too long to decide what to do next, an opponent might steal the ball, or the robot which is dribbling with the ball simply loses the ball because it stopped to think about when to kick the ball. This example shows that the real-time aspect of a domain is connected to several other aspects like acting in an adversarial domain. This aspect has an impact on the high-level decision layer: if it takes too long to decide, an opponent which decides faster will render the own efforts useless. If no opponents

are involved there still are real-time aspects for the whole system. The example of dribbling a ball shows that motion control of the robot must also take decisions in real-time. If there are time latencies during execution, the robot will simply lose the ball while dribbling.

Many researchers intensively think about the term physical embodiment. For example, the Cognitive Robotics community discusses if a system acting in the real world must have a rigid body to give realistic results. Our experience over several years of doing RoboCup shows that it has significant impact on the design of decision making algorithms if the agent you deal with is physically embodied. Many good ideas turn out to be not feasible in the real world application. It also forces the system designer to meet the hard reality. Even in good simulated environments it is often not possible to generate results which are realistic enough to transfer the results directly to a real world application.

The soccer domain is only partly observable. The robot can only observe several aspects like its own position or the ball position. For instance, it may be that the robot cannot perceive what is happening behind it. For a simulated environment it means that not all important aspects are accessible.

Uncertainty is imposed by several other aspects of the robotic soccer domain. One reason for uncertainty for a soccer robot comes from the aspect of embodiment: we are dealing with “real” systems in the real world. This means that the actuators and the sensors of the robot are error- prone. The sensors are not accurate and thus impose uncertainty on the robot system. For example, consider the estimation of the position of the robot on the soccer field. The autonomous robot can only estimate its own location. The actuator system of the robot is imprecise and moreover coupled to the error-prone sensor systems. If the robot kicks the ball it can never predict exactly where the ball will be afterwards. The reason lies in the fact that we can only partly observe our domain. Many relevant aspects of the domain are not accessible to the agent. Again, consider the kick example. It will also depend on the pressure inside the ball where the ball will be after the kick. This information will never be available to the robot. In a simulated environment these problems are not ostensible. For this reason noise functions are used to simulate these effects also in simulated agent systems. Another kind of uncertainty is imposed by the fact that robotic soccer is an adversarial domain. The robot does not know the behaviors of its opponents. It can build models for the possible behaviors by observing them, but these form of prediction is also uncertain. The behaviors of the opponent should have direct influence on the decision making of the robot.

For many problems a winning strategy exists. One can prove that with this strategy the goal can be reached. For example, in checkers such a strategies is known. In a game like robotics soccer there definitely does not exist a global strategy which ensures to win the match. The term strategic domain also covers if there are strategies or tactics to achieve desired sub-goals of a game. Achieving these sub-goal do not necessarily ensure the achievement of the global goal, but might build a good base in doing so. For example, think of a defense strategy in robotic soccer. With a good defense which hinders the opponent team to shoot goals it is more likely to win a soccer

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match than without. The question is if such strategies for a domain exists, and which they are. In domains like soccer or interactive computer games like UNREALTOURNAMENT2004 adversaries try to foil the endeavors of the agent to reach its goal. It introduces another source of uncertainty to the agent as it is hard to predict how the opponent might behave. It fosters the design of flexible and general approaches for achieving the desired goals of the domain. Another important aspect of an application domain for an agent system is the cooperativeness. This aspects is about if the global goal can be reached by one agent or of several agents need to cooperate in order to achieve the global goal. The aspect of cooperation has a major impact to the design of decision making. The design of the high-level control of an agent is also influenced if the domain is partly episodic. By this we mean that there are episodic elements in the application domain. For soccer this means that there are standard situations like free kicks or corner kicks. These situations define a subspace of all possible situations. For these episodes more specialized strategies can be developed.

After having introduced these domain properties of robotic soccer we now come to our first soccer application example. Throughout the rest of this thesis the soccer example will be our companion.