Robocup: 10 Years of Achievements and Future Challenges

AI Magazine Volume 28 Number 2 (2007) (© AAAI) Articles RoboCup: 10 Years of Achievements and Future Challenges

Ubbo Visser and Hans-Dieter Burkhard

■ Will we see autonomous humanoid robots that although they can use their bodies, they must play (and win) soccer against the human soccer not cause harm to humans. The robot’s per- world champion in the year 2050? This question formance must therefore be restricted in order is not easy to answer, and the idea is quite vision- to adapt to the situation of cooperating with a ary. However, this is the goal of the RoboCup Fed- human, for example. The robot should have eration. There are serious research questions that soft surfaces rather than a hard metal or plastic have to be tackled behind the scenes of a soccer game: perception, decision making, action selec- body, it should have roughly the same weight tion, hardware design, materials, energy, and as a human, it should not be faster than a more. RoboCup is also about the nature of intelli- human, and so on. In a way, the situation is gence, and playing soccer acts as a performance like conducting the Turing test while restrict- measure of systems that contain artificial intelli- ing the computer to a certain computational gence—in much the same way chess has been power. If indeed we are able to create machines used over the last century. This article outlines the of this kind, then we will also be seeing them current situation following 10 years of research in everyday situations. We may see robots with reference to the results of the 2006 World working with the fire brigade, sitting in a street Championship in Bremen, Germany, and discuss- car, and so on. Technologically, these new es future challenges. robots will require the development and application of new materials, sensors, and actuators. A World Championship In addition, we must also address the issue of of Soccer-Playing Robots energy. RoboCup is thus an interdisciplinary long-term project. Starting with the vision of the RoboCup Feder- Many researchers in the community are con- ation, we briefly discuss the major differences fronting the question of whether we will actu- from former AI challenges and outline the fed- ally be able to achieve this vision one day. The eration’s statement. The last section consists of uncertainty is part of the attraction of the chal- some statistics for the RoboCup competitions. lenge. Looking back, for example, to the air- craft industry of 100 years ago, researchers The Vision could then only dream of the machines that The vision of robots playing against humans nowadays carry millions of passengers through has presented many challenges. Robots should the air every year. Back then, researchers also be able to handle situations without the need had to find new materials and new technolo- for human assistance. They have to interpret a gies. Competition played a significant role and given situation and use their skills accordingly. was one of the driving forces behind the devel- Given the nature of this dynamic environ- opment. ment—which demands decisions in real time—background knowledge is also essential. Chess or Soccer? This requirement has been underestimated for One of the primary questions RoboCup raises is a long time now and is especially needed if that of the nature of intelligence. Some 50 robots are one day to play against humans. years ago, we believed that intelligence Robots should act appropriately, too; that is, depended on the speed of computation. The

RoboCup is an international joint project to promote AI, robotics, and related fields. It is an attempt to foster AI and intelligent robotics research by providing a standard problem where wide range of technologies can be integrated and examined. RoboCup chose to use soccer game as a central topic of research, aiming at innovations to be applied for socially significant problems and industries. The ultimate goal of the RoboCup project is ”By 2050, develop a team of fully autonomous humanoid robots that can win against the human world champion team in soccer.“ In order for a robot team to actually perform a soccer game, various technologies must be incorporated including: design principles of autonomous agents, multi-agent collaboration, strategy acquisition, real-time reasoning, robotics, and sensor-fusion. RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. RoboCup also offers a software platform for research on the software aspects of RoboCup. One of the major application of RoboCup technologies is a search and rescue in large scale disaster. RoboCup initiated RoboCupRescue project to specifically promote research in socially significant issues.

Figure 1. The RoboCup Statement.

ability to play chess for example was consid- uation platforms in the creation of new solu- ered to be a key to understanding intelligence. tions. Everyday intelligence—something everybody Another area for autonomous robots is a is capable of—has been totally underestimated, catastrophe scenario in which robots locate and as a result, many of the dreams for AI from victims in areas that cannot be accessed by the 1950s failed. A comparison between chess humans (mostly due to dangerous situations and soccer reveals that the requirements for such as gas, heat, collapsing buildings, and so playing soccer are closer to those needed in on). RoboCupRescue offers a thorough test everyday situations. It is also significantly platform for this kind of scenario. Another harder to meet these requirements when using application area is that of everyday environ- computer-controlled machines. ments such as a typical household. A new test Chess has inspired the effort to create a bed that requires special skills was demonstrat- machine with artificial intelligence, and there ed at RoboCup 2006: RoboCup@Home. Robots have been various approaches to achieving this had to fulfill tasks such as opening a door, fol- goal. In the end, the brute-force approach was lowing a human, and so on. The vision also successful, resulting in the 1997 match in includes taking care of the next generation. which Deep Blue beat Garry Kasparov. The One of the areas of RoboCup is therefore complexity of chess is undoubtedly high. In designed for children and younger people: the end, however, search methods were able to RoboCupJunior. handle these requirements. Feuilletonists could, however, comfort the shocked audi- Competitions ences with the statement that the computer The first RoboCup world championship was was not really intelligent as such. held in Nagoya, Japan, in 1997. Annual competitions, accompanied by a scientific sympo- Soccer-Playing Robots: RoboCup sium, have been carried out ever since, and the Computer programs that can play chess did community is still growing (see figure 2). Start- not, however, answer the question regarding ing with 42 teams in 1997, by 2006 there were the nature of intelligence. Dealing with every- approximately 450 participating teams. In day situations, the combination of and inter- 2006, the number of participants exceeded the action between body and intelligence and the 2500 mark. With a total of 2561 participants— integration and combination of various meth- 1492 in the senior and 1069 in the junior com- ods were challenges in the mid-1990s. petitions—RoboCup is one of the largest (if not Researchers were seeking a new long-term the largest) robotic and AI events in the world. vision, and thus the idea of RoboCup as a test- Soccer-playing robots attract media, and the bed for AI and robotics was born (see figure 1). RoboCup Federation therefore also takes large Friendly competitions have often driven sporting events into account when choosing technical advances—the automobile and air- the locations for the annual event (for exam- craft industries provide prominent examples. ple, World Cups 1998, 2002, 2006, European Competitions have been used as test and eval- Cup 2004, and the Olympic games 2008).

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The Robot Leagues There is still much to accomplish before we can hope to achieve the 2050 goal. In order to com- 500 pete and indeed cope using the hardware and the technologies from today, several leagues have been created. In 1997 RoboCup had three leagues, the Middle-Size League (MSL), the 375 Small-Size League (SSL), and the Simulation League (SL). Today, with the addition of the Four-Legged League (4LL) and the Humanoid 250 League (HL), the number of soccer leagues is now up to five. Parallel to the soccer leagues, other leagues were also invented in order to tackle other 125 applications. The RoboCupRescue League was established in 2001 with robots and simulation devoted to rescue scenarios, while the compe- 0 titions in RoboCup@Home (established in Bre- men) demonstrate robots used for daily activi- 19971998 ties. The RoboCupJunior leagues include 19992000 20012002 competitions in soccer, rescue, and dance. 2003 2004 2005 2006 They are designed to foster education in schools and are tailored for students aged 8–18 years. The Middle-Size League (MSL) Figure 2. Number of Teams at the RoboCup World Championships. In the middle-size league (MSL), middle-size robots must not exceed a diameter of 50 centimeters, a height of 80 centimeters, and a of the teams have kicking devices that allow weight of 50 kilograms (see figure 3). Each them to kick high and pass over the opponent robot has individual sensors and control pro- robots. This makes a whole new range of tacti- grams. Communication between robots can be cal options possible. achieved with a wireless local area network Most of the teams in the MSL have powerful (WLAN). It is also possible to communicate laptops on board. This also means that compu- with an external computer. tationally expensive methods can be used as Since 1997, several steps have been taken to opposed to a robot in the 4LL, for example. accommodate various new challenges. In 2002 Thus, the most powerful image-processing for example, the barriers around the field were methods can be found in this league. The removed and the field size was increased. The demand for environments less conformed for lack of barriers also meant that certain sensors robots has also now been met, for example, used for localization (ultrasound or laser) could with regards to illumination. Since 1997, light- no longer be used for that purpose. Nowadays, ing conditions have been defined (light, colors, techniques based on vision (mostly omnidirec- ball, goals). As a consequence, methods have tional cameras with 30 frames per second or improved. In 2006 for instance, we already had more) take on the task of localization. The six variable lighting conditions with one half of robots per team are able to use separate blue or the field receiving natural light from outside. yellow colored goals, colored landmarks, and Kalman filter methods seem outdated for use white lines on a green field for orientation. in this league due to the fact that Monte Carlo Removing the barrier was a big step, given that is gradually taking over localization in practice. the robots then had to control the ball to avoid The robots can localize various objects in real kicking it out of the field. time now using relatively inexpensive standard MSL reports substantial technological hardware. Also with regard to controlling the advancements. Some teams use big, powerful robots, for example, applying defending and robots with strong kicking devices. Over the attacking maneuvers, the teams are in good years, however, the use of those types of robots shape. Some attacks, which combine dribbling has faded. The tendency now is to build small- and simultaneous ball control and high-speed er, faster, and lighter robots. Despite other obstacle avoidance, are breathtaking to watch, models in the past, an MSL robot nowadays is when measured by any standard. Multiagent omniwheeled and omnivisional. The majority scheduling, assignment, and role-picking prob-

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Figure 3. Middle-Size League Robots. Left: MSL robots from 1997. Right: Robots in the World Cup final 2006.

lems are well understood and can be solved for game running. By 2006, many teams have suc- standard situations. Realistic simulations ceeded in reaching this level of play. enabling the performance of coarse-grained Increasing the field size also forced teams to training (and learning) have now become pos- use more than one camera. This brought on sible. The developers of the champion Brain- new challenges concerning the development stormers Tribots from Osnabrück (Germany) of techniques for sensor fusion and the effi- were already well recognized for their effective cient processing of growing amounts of image results using machine-learning techniques in data. the Simulation League. Starting exclusively with the fact that robots For 2007 the aim is to play outside in the have different drives, robot design has evolved open field. Another challenge will, however, be incrementally. The current state-of-the-art the integration of mixed teams from different design for a robot is one with four (in most cas- labs, which when combined on site must coop- es self-made) omnidirectional wheels, enabling erate in applying their soccer knowledge. velocities of up to 2.5 meters per second to be reached (see also figure 4). The rules concern- Small-Size League (SSL) ing ball-handling mechanisms (kickers, chip Robots in the Small-Size League (SSL) must not kickers, and dribblers) have continually been exceed 15 centimeters in diameter. There is a adapted over the years in order to prevent inco- central computer that determines the actions herent and rough game play and to enforce of the whole team, with each individual robot more intelligent team tactics. being controlled by radio communication. Two In general, we have seen a high level of tac- cameras (one for each half of the field) deliver tical game play by the majority of the partici- a bird’s eye view of up to 100 frames per sec- pating teams. Most teams were able to play pre- ond, making it possible for the exact positions cise passes and to perform well-directed chip of the robots to be viewed as well as determin- kicks and intelligent defense behaviors (for ing the position and velocity of the ball. Given example, one-on-one defense). Some of the the central control of the team, good coordi- top teams even displayed capabilities known as nation can be achieved. “one-touch soccer” (“1-2-3 passing”), which Since 1997, the environment has undergone means that following a pass, the ball is directly enormous change. For example, the field size shot once again without any interference. The is now four times as large as it once was. Most successful application of such capabilities of these alterations took place in 2004—when, demands a high level of teamwork and robot beside a change of the field size, the walls were control. The 2006 tournament was significant- removed and auxiliary lights banned. In the ly determined by these capabilities rather than first year following this radical change, most by the implementation of simple and straight- teams performed quite badly. Due to the high forward strategies. speed of the robots and the power of most kick- Adaptive team strategies, estimation of ing mechanisms, a high level of control and opponent strategies, and fast cooperative play intelligent game play were needed in order to such as passing and shooting have also been keep the ball on the field and thus keep the successfully tackled in the small-size league.

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Figure 4. Small-Size League Final. (5dpo, Portugal versus CM Dragons, USA)

There is however as yet no overall solution, the physical world including the bodies of the since dealing with a whole team of robots players. The server transmits interpreted sensor (from the design of the hardware to the devel- information to the programs of the players, opment of the software) is challenging in itself that is, their “brains.” The server therefore per- and requires many resources. However, with forms simple perception actions. Players have a fast and accurate game play, the SSL games restricted view of the scene (adapted from have been quite engaging and entertaining this humans), and the received information is year. This has also been reflected in the large noisy. The players’ programs can generate an amount of positive feedback and enthusiasm internal view of the situation and can decide received from the outside community. on the next action (for example, dash, kick, In 2006, the winning CM Dragons’06 turn, each of them with a certain power and (Carnegie Mellon University) team reached a direction). The server then takes these actions new stage of performance with the use of pre- and executes them in the virtual playing cise hardware and sophisticated software. The ground. A soccer monitor can be attached in team’s robots were able to pass with a high ball order to view the game. speed (up to 2.5 meters per second) over sever- This league implements a multiagent sys- al opponents directly to the “feet” of another tem, and the scenario offers research potential teammate with a precision of several centime- in various areas such as coordination and coop- ters. eration, distributed planning, learning of vari- One of the future challenges for this league ous levels (skills for a single player and team is an 11 by 11 game on a larger field. Particu- behavior), and opponent modeling. To prevent larly concerning robot vision, important issues central control, each player program has to act are also color correction for lighting variations independently. Communication with the use across different fields, multicamera fusion, and of short messages is only permitted using a say- robust tracking for handling ball occlusions command through the soccer server. These and estimating flight paths of high kicks. The programs are available online (sserver.source- SSL also must address latency modeling (a good forge.net) and can be downloaded. The league team has a latency of approximately 110 ms) started with a two-dimensional simulation and and prediction methods to account for latency. is now progressing towards a three-dimensional simulation containing a more realistic physical simulation (ODE). Simulation League (SL) The best teams can now show real soccerlike The Simulation league consists of a virtual coordinated behavior. Successful attacks from playing ground with the original soccer field the wings, offside traps, strategic positioning, size and 11 virtual players per team. The main and open-space attack instead of direct passing challenge here is to deal with the problems of are just some examples of their improving skill. cooperation among 11 players. This league was In two-dimensional simulation, cooperative established to explore strategic and tactical defense ability has improved over the years. It behavior of a team. A soccer server simulates seems that not only individual skills, such as

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an interception or marking, but also coopera- the results of the 2005 and 2006 competitions, tive behavior, such as strategic positioning, the teams’ abilities in modeling and the detec- have been improved. As a result, the number of tion of patterns had improved. There was tie games has increased, and the differences marked progress in feature selection and gen- between teams in terms of goals scored have eration, and also in behavioral pattern model- decreased. To break such strong defenses, the ing and detection. However, there is still much top-level teams have started implementing to be done. more cooperative offense strategies, similar to The coach competition initially started in those seen in human soccer. 2001. The goal at the time was opponent mod- In the three-dimensional league (figure 5), eling and team adaptation. Between 2001 and the top teams had highly developed low-level 2004, the measure of the coaches’ performance skills (ball interception, passing, dribbling, was based on their score differences. Merely high and low goal shots). Some of the best scoring more or receiving fewer goals did not teams employed open-space attack strategies force teams to apply opponent modeling, so instead of direct passing. We also saw the they only used a static hand coded list of pre- increasing use of high passes (especially from defined amounts of advice. In 2005 and 2006 the wings to the goal area) and high goal shots the structure of the coach competitions was (also due to increased goal height). The cham- changed so that teams had to detect different pions of the 2D and 3D leagues, Wright Eagle playing patterns activated in the opponent (University of Science and Technology of Chi- team. na), and FC Portugal, played exciting games. The next challenges in the Simulation Coach Competition League concern further improvements of long- Each team is allowed to use a coach program, term coordination as well as the adaptation of which can analyze the ongoing game using a opponents using the coach and automated global view. Such programs can provide advice analysis of earlier games. Players should be able to its team during the breaks (for example, to synchronize their intentions. To get closer to while the ball is outside the field lines), and can the real robots, new simulation efforts (for substitute players (a maximum of three example, simulating humanoid robots) are times)—an important development given that needed. players get tired and have varying levels of skill with regard to kicking, running, and power. A Four-Legged League (4LL) special coach competition has been established Sony’s AIBO is the robot that makes this league. to evaluate intelligent advice from the coaches. One of the reasons for the existence of this In addition, automated commentators exist league is its platform independence. Every and are used to provide live commentary on research team has the same type of robot; thus games in an appropriate manner. there are no hardware development issues. The coach programs within the coach com- Thus the 4LL is, in fact, a software competition petition begin by analyzing the previous-game built for the 576 MHz robots. The AIBO robot log files of a given fixed two-dimensional team. type ERS-7 comes with a wireless LAN and has The strategy used by the coach consists of 20 degrees of freedom, and the joints can be several patterns. In the first step (offline analy- controlled with a frequency of 125 Hz. A cam- sis), the coach has to detect specific patterns era placed at the front of the head offers 30 (simple behaviors) from the log files. In the frames per second with a resolution of roughly next step (online detection phase), the coach 300,000 pixels. Before ERS-7, the teams used must detect patterns that are activated in the ERS-110 and ERS-210 models. The newer types fixed team. The coach then advises its team provided better on-board computer and cam- during several full matches against a fixed era performance but also have differing body opponent. The coaches should then detect the designs. Therefore, skills like walking and kick- play patterns of the fixed-opponent in each ing have had to be redesigned. This change has game and report on them. The coach can send forced the teams to develop related tools using useful advice to its team members to ensure machine learning. that the detected patterns are correctly fol- As is the case in other leagues, the rules of lowed. The performance of a given coach is the game have changed over the years, always based solely on its ability to detect these pat- keeping up with cutting-edge technology. The terns. The year 2006 marked the second year in field size has increased to 6 x 4 meters, the which the coach competition was held using walls and the sight protection (see figure 6) this format. have been removed, and the number of land- The winner of the coach competition in marks has decreased. 2006 was the MRL team from Iran. Comparing The robots are able to perform numerous

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Figure 5. Current View of a Three-Dimensional Simulation League Game actions due to the number of joints and motors it of RoboCup that solutions and programs are (three actively controlled DOF per leg). The ball exchanged between the various teams. can be kicked in almost every direction that it We can also now claim new and advanced is possible to carry the ball (but only for short technological skills. In 4LL, topics and periods of time as governed by the rules), and advancements include low-level behaviors, spectacular overhead kicks are also possible. learning, motion detection, and motion mod- This variation in skills supports the develop- eling. This year, the old question of whether to ment of methods for planning and action use a Kalman filter or a particle filter for local- selection. ization was clearly won by the Kalman filter, Begun in 1999, this particular league com- since both finalists employed this method. prises the most basic skills among the hardware Interestingly, 4LL differs from the Middle-Size leagues. The 4LL also attracts spectators of all League, in which Monte Carlo–based methods ages and is therefore an important league in are preferred. A step towards collaborative RoboCup. However, the announcement by world modeling was demonstrated in the pres- Sony in the spring of 2006 that it was stopping entation of the Microsoft Hellhounds where a the production of AIBO portends a change in blind robot was controlled by two seeing ones. this league. RoboCup Federation is still cur- The German Team showed that AIBO robots rently seeking a hardware platform that can could actually play soccer with a black-and- replace AIBO—one that is just as ambitious in white ball instead of an orange one. This is terms of its perception and action capabilities. challenging because the on-board camera of an Since every research team works on the same AIBO is rather limited. TsinghuaHephaestus, a hardware platform, the focus of this league team from China, demonstrated the ability to concentrates on perceptions and on methods localize on the field without colored land- of controlling the hardware. The robots are get- marks. ting faster and faster every year due to the A demonstration game with 11 AIBO robots improved controls, which are developed using per team on a middle-size field was held in Bre- machine-learning methods. The robots can men for the first time at a RoboCup World now reach up to 50 centimeters per second. Championship. The existing programs from This common hardware platform also fosters the 4LL had been adapted to the larger field scientific exchange—it belongs too to the spir- size. The experiment demonstrated individual-

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Figure 6. A Scene from a Semifinal of the Four-Legged League 2006. rUNSWift (Australia) Versus Microsoft Hellhounds (Germany).

istic styles of play, since no efforts had been remotely controlled and should not have fallen taken to improve cooperation with more play- down under any circumstances. ers. Nevertheless, localization and ball han- The RoboCup Humanoid League started off dling were performed as well as on the small in 2002 with demonstrations of single skills field. such as walking or a penalty shoot-out. These The champion NUbots (University of New- technical challenges still belong to the compe- castle, Australia) team was superior due to its tition; however, since 2005 we have also seen 2 precise perception and action. Most of its versus 2 games on a field that is similar to the matches ended with very clear results. It is 4LL field in terms of size and colored land- interesting to note, too, that in all real robot marks. leagues the results of most matches appear to The robots on the field have to act be very clear-cut, for example, 6:0 or even autonomously as in all RoboCup soccer leagues. more in only 20 minutes. This occurs even Human intervention is not allowed, and even between very good teams. These results are pos- when robots fall down (which happens very sibly explained by the small fields on which often), they must get up by themselves (other- teams can often exploit small advantages in a wise they are penalized). More than 30 percent straightforward way. In contrast, matches of the kid-size robots were able to accomplish between above-average teams in the Simula- this in 2006. Goalies were able to jump or fall tion League often result in draws. into one of the corners of the goal in order to catch the ball (first seen in 2004, two years after Humanoid League the start of the league). Most robots were also A humanoid robot has humanlike proportions able to kick the ball with force without loosing and appearance: one head, one body, two balance. Some teams implemented multiple arms, and two legs. Two different robot sizes kicking behaviors applied to different situa- exist—the child-sized robot with a maximum tions. The first teams also implemented stabi- height of 60 centimeters and the teen-size lizing reflexes such as stop walking, which is a robot, which has a maximum size of 120 cen- technique used to attempt to regain balance timeters. The robots are also restricted to a when instability is detected. Some teams also maximum foot size, and a minimum height of implemented protective behavior if a fall was the center of mass or the length of the arms. seen as unavoidable. The quick technological advancements Most teams used motion macros with brief within the RoboCup community can be best stops when changing walking direction and for described in this league. In 1998, during the capturing images. In contrast, team NimbRo RoboCup World Championships in Paris, Hon- (University of Freiburg, Germany) used omni- da’s P2 (predecessor of ASIMO) kicked the ball, directional walking, which enabled its robots while the world watched. The robot was to change walking direction and walking speed

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Figure 7. Humanoids at RoboCup 2006. These robots are dominated by constructions based on aluminum, carbon, and servomotors. Prototypes with alternative concepts like artificial muscles (the gray in the left background) are exceptions and were not competing in this case. smoothly while continuously receiving visual angle cameras, and the remaining teams used a feedback. movable narrow-angle camera to keep track of In contrast to the other real robot leagues, the ball, the other players, and the field land- there has been, up to now, no convergence to marks. No special lighting was used for the standard solutions. The construction of fields, and only the ceiling lights of the hall humanoid robots differs from team to team. were turned on. Most vision systems were able Some teams use low-cost construction kits. to tolerate significant additional daylight com- Other teams purchase more expensive com- ing through the windows. The 2 versus 2 finals mercial humanoid robots. However, most were played on the center court, where the teams construct their robots themselves. While vision systems had to adapt very quickly to dif- most robots were actuated with the help of ferent lighting conditions. Complex mechani- small servomotors, the 140 centimeter robot of cal compositions are responsible for new chal- Pal Technology (figure 7, the black robot on the lenges in perception. One of the problems is right in the background) used harmonic drive the determination of the camera position and gears. The teen-size robot, Lara of Darmstadt its direction. Dribblers, was constructed to use antagonistic The 2006 champion was once again the win- shape memory wires as muscles (see figure 8, ner from last year, Team Osaka from Japan with the large robot on the left). the robot Vision. It was a dramatic final—the Perception (especially vision) of the game first half had a clear result—4:0 for Team Nim- situation is realized using methods that have bRo from Freiburg—but Team Osaka was able been adapted from other leagues. However, to reach a draw in the second half and finally they are not able successfully to avoid colli- to win the game by 9:5 in over time. sions and contact with the ground. While Future developments will at first concern some teams used an omnidirectional camera as more reliable and flexible skills. Given the his- the main sensor, others relied on directed wide- torically fast developments in RoboCup, we

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should see substantial progress during the next in order to maximize the overall number of few years. The next step will be the integration identified victims. Difficulties in cooperation of developments from other leagues. Neverthe- between robots from various teams arise due to less, for competitions to reach the level of the differences in hardware and software. human players, a long period of development Achieving cooperation between robots in this is needed—if indeed this is possible at all. New league is therefore a significant step forward. materials and training methods must be inves- A new subleague in the Rescue Robot League tigated, with the current perception still on a was introduced in 2006: the Virtual Robots basic level. Thus, RoboCup will remain a big Competition. This competition is based on the laboratory for investigation and integration of Urban Search and Rescue (USAR)-Sim code different fields. developed at the University of Pittsburgh, Increasing overall system robustness is also a which in turn is based on the game engine major challenge. On the mechanical side, bet- from the commercial computer game Unreal ter actuators are needed that can tolerate shock Tournament. USARSim allows high fidelity loads, stronger skeleton structures, and soft simulations of multirobot systems. It currently protective covers. Professional electronic offers the possibility to simulate commercial as design and more emphasis on system integra- well as self-developed robot platforms. USAR- tion and testing is also needed, as is an overall Sim complements the rescue league in an ideal increase in reliability. On the software side, way, with a realistic physical simulation of adaptiveness and learning could be used to teams of robots operating within collapsed improve system robustness. buildings. On the one hand, it offers the possi- Most of the institutions participating in bility to simulate search and rescue scenarios in RoboCup will probably be unable to sustain which every agent has capabilities comparable the construction of a complete team of sophis- with those found on real robots, such as sens- ticated humanoid robots over a long period of ing with laser range finders or heat sensors. On time. As a result, a combination of different the other hand, it opens the door to investi- humanoids may be developed at various insti- gating aspects of autonomous multirobot tutions, bringing together one team for the cooperation on the “sensor level” within future. Such cooperation will be a challenge in unknown and unstructured domains. The lat- itself, let alone the numerous hardware and ter aspect is of particular interest given the software issues that must also be addressed. physical robots in the rescue league, which more and more allows for the development of RoboCupRescue Leagues autonomous systems. Disaster rescue is a serious social issue, which The virtual robot competition offers a wide in the case of RoboCupRescue involves a large range of challenges since each team can choose number of heterogeneous agents in a hostile the number of robots, the type of robots, as environment. There are two leagues operating well as whether they have an operator or run in this arena: the Rescue Robot League and the robots completely autonomously. As has been Rescue Simulation League. The primary goal of seen this year however, the difficulty of run- the RoboCupRescue League is to promote ning a large number of robots that face nearly research and development in this socially sig- the same problems as real robots at the same nificant domain at various levels, including time, such as simultaneous location and map- multiagent teamwork coordination, physical ping (SLAM), exploration, and coordination, robotic agents for search and rescue, informa- seems to be enough of a challenge. Some teams tion infrastructures, personal digital assistants, for instance have in particular focused on team a standard simulator and decision support sys- coordination with more than 10 robots and the tems, evaluation benchmarks for rescue strate- online merging of maps. Simulation environ- gies, and robotic systems that are all integrated ments are significant for the development of into comprehensive systems for the future. physical robots in every league. Almost all Integration of these activities will create the teams develop simulators over years. This year digitally empowered international rescue however, teams in the Rescue Robot League brigades of the future. have begun using simulators to validate their Rescue Robot League control algorithms. The competition field itself now has many gaps Rescue Simulation League and debris and requires cooperation among the The rescue domain represents a real multiagent robots. In previous years the robots did not scenario since a single agent cannot solve most have to cooperate, as there was only one robot of the encountered problems. Fire brigades, for in the arena. In the Rescue Robot League (fig- example, depend on police forces to clear ure 8), robots now cooperate with each other blocked roads in order to extinguish fires (see

124 AI MAGAZINE Articles figure 9). Moreover, the task is challenging due to the limited communication bandwidth, the agent’s limited perception, and the difficulty of predicting how disasters will evolve over time. Therefore, this domain requires a high level of team cooperation and coordination, and so far no general solution has been found. The purpose of the infrastructure competition within the Rescue Simulation League (RSL) is to foster the development of software com- ponents for simulation, such as the simulation of the spread of fire. This is necessary because the teams actually compete against a disastrous environment rather than against each other, as is the case in other leagues. During the infrastructure competition this year, human-in-the- loop interface extensions for the simulator ker- nel were introduced. These become particularly relevant in utilizing the simulation system for real disaster mitigation, such as the training of incident commanders and disaster data integration. Figure 8. A Robot from the Rescue Robot League In the RSL, three major breakthroughs have Finds Its Way through the Step Fields emerged over the last two years. First, a new, more realistic fire simulator has been intro- tions in daily life circumstances. In addition to duced, enabling greater agent challenges. For its value as an effective demonstration tool for example, buildings can now be preemptively the general public, as well as being an appeal- extinguished. Other improvements include the ing scenario for both the press and television, increase in the Kobe city map scale from 1:10 the Rescue Robot Field Test has served as a test to 1:4, plus a revised and more advanced pres- bed for the development of advanced robots by entation style of simulation. This new format various academic institutions and, to a large presents the simulation on three separate extent, students and other young scholars. screens—one large screen displays the run of RoboCup@Home each team on the map in three-dimensional format, and on two smaller screens, a two- RoboCup@Home, first begun in 2006, focuses dimensional view and statistical information on real-world applications and human- are displayed. machine interaction with autonomous robots. Furthermore, locomotion in three-dimen- The aim of the league is to foster development sional rescue simulation, for example, reach- of useful robotic applications that can assist ing other floors by using stairs or climbing ran- humans in everyday life. The league consists of dom steps, present further challenges— a series of fixed tests and an Open Challenge. challenges that form the basis for real robot The ultimate scenario is the real world itself. To teams to operate autonomously within the real gradually build up the required technologies, a world in three dimensions. The simulation basic home environment is provided as the competition also opens the door for researchers general scenario. In the first years it will consist to overcome the enormous technical hurdles of of a living room and a kitchen (see the setup in running real robots as a team and having to figure 11), but soon it will also cover other prepare their robot architectures, even in large areas of daily life, such as a garden or park, a scale environments. shop, a street, or other public places. Figure 12 A special outdoor demonstration (the Rescue shows a living room and a dining room with a Robot Field Test) was organized as a special robot during the competition in Bremen. This event at RoboCup 2006 in Bremen, where test will become more advanced over time and mobile robots and fire brigades worked togeth- serve as an overall quality measurement in the er in a simulated hazardous material accident desired areas. The tests should feature human- (see figure 10). As an opening attraction of machine interaction and be socially relevant, RoboCup, the Rescue Robot Field Test con- application oriented, scientifically challenging, tributed to the general success of the champi- easy to set up, cost effective, simple, user onship by demonstrating the advantages of friendly, time efficient, and, lastly, be interest- robot technologies and their potential applica- ing to watch.

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Figure 9: Scene from the Rescue Simulation League.

In the open challenge, freely chosen robot machine interaction. Human-machine interac- abilities can be displayed and proposals for tion was demonstrated with the use of natural future tests can actually be presented to the language commands. Communication too league. According to the criteria of between the scientists and the audience during RoboCup@Home, a jury then determines the the competition has shown to be a successful score and rankings. All robots participating in way to provide an understanding for the moti- the RoboCup@Home competition have to be vation behind the robot technology presented. autonomous. During the competition, humans During the competitions, robots were able to are not allowed to directly (remote) control the cope well with the natural lighting conditions robot, but natural interaction is allowed. So for and environment. The robots were able to nav- example, joystick, mouse, and keyboard con- igate in unstructured and stochastic environ- trol are not allowed, but speech and gesture ments and to manipulate real objects. commands are. For the early years, headsets As the tasks in the Open Challenge and the will be permitted. Decentralized computing finals are not predefined, a jury had to evaluate will also be allowed but may be difficult to the performances by use of defined evaluation achieve given general communication prob- criteria. This criteria included presentation, rel- lems that could occur during the course of the evance to daily life, human-robot interaction, competitions. The real world presents a high applicability, ease of setup, autonomy, difficul- degree of uncertainty, dynamic changes, and ty, originality, success, and scientific value. The variation. In RoboCup@Home environments, a winner of the first RoboCup@Home competi- robot has to deal with all these factors. The lev- tion was the AllemaniACs team, from el of uncertainty will of course too increase Rheinisch-Westfälische Technische-Hochschule over the years in order to adapt to the current Aachen, Germany, which used one of its MSL environment. robots to perform in this league. The jury was Eleven teams participated at the 2006 com- particularly impressed by the precise, fast, and petitions in Bremen. Five of them were new to robust navigation displayed, including dynam- RoboCup and had a background in human- ic path planning and obstacle avoidance.

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Figure 10. Mobile Robots Work Together in a Simulated Hazardous Material Accident. Team Germany 1 is a joint German RoboCup rescue project from University of Osnabrück, University of Hannover, and Fraunhofer IAIS. The team demonstrated two platforms at the outdoor event. The robot shown on the picture is based on a commercial platform from Telerob.

It is, however, worth mentioning that 45 different gray scales enabling orientation to be percent of the registered teams within this performed using simple light sensors. The ball league actually had a background in human- emits infrared signals, and simple light sensors machine interaction, with the others stem- can localize the ball. The rescue competition ming from the robotics area. Communication consists of line-following with obstacle avoid- and exchange between these teams and the ance and identification of objects. The course teams already active in RoboCup will on one also includes an inclined plane. The dance hand help them to foster an understanding of competition is a free-style competition, in the special demands of a robot competition which the robots can perform dancing or act- (with respect to robustness of the robot systems ing according to a particular story. Often the and the organization of teams) while on the performances include both humans and robots other hand will allow for a gaining of new (see figure 13). A jury then decides on the win- input from the field of human robot interac- ner based on various criteria such as technical tion (which, up until now, has not directly design, performance value, and aesthetics. been addressed in the RoboCup initiative). The junior teams may use commercial kits Issues encountered during the RoboCup@ but are also able to use their own designs. The Home Competition include intuitive, human- market for related kits is growing in both size machine interaction, manipulation of physical and technical aspects (following the develop- objects, and people recognition and tracking. ments of technology), and therefore RoboCupJunior is of great interest to commer- RoboCupJunior cial firms, particularly in Asia. Numerous More than 1,000 young people in 239 teams awards are given to encourage and motivate from 23 countries participated in the development within this area. Recognition is RoboCupJunior competitions in soccer, rescue, particularly given to those mixed teams from and dance. Soccer is played with one or two different countries, which were actually robots per team, and the playing ground has formed during the competition.

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10.00 .04.00 3.00 Kitchen Living Room 4.50

Garden 6.50

3.50

Figure 11. The Setup of RoboCup@Home. Source: RoboCup@Home rulebook.

RoboCup Competition es by allowing robot play in ordinary environments such as sports halls (as seen in 2005 and Research within the German AI conference) and outside The combination of competition and science on the green (planned in 2007 for the Middle- within the title “RoboCup Championship and Size League). Symposium” highlights the scientific claim Ideas, solutions, and also complete software that the competition is one form of evaluation programs have been published providing new in which ideas and methods are exchanged. In teams the chance to obtain the best methods fact, RoboCup is a platform for (mostly young) for use on their robots. A prominent example is researchers, and its success and fast technolog- the code of the German Team (4LL), which ical advancements over the last 10 years have won the championships in 2004. A number of shown that the concept is effective, intelligent, teams downloaded the software from the Ger- and extremely relevant. The concept is based man Team’s web page and were then oppo- on a number of points, which are discussed nents at the same level in 2005. The winner’s next. disadvantage in publishing their secrets is out- Long-term goals are written down and are weighed by the references and citations and, maintained from year to year with a roadmap, therefore, their enhanced scientific reputation. which is based on the vision of 2050 but also This is one of the goals of the federation. In considers current technical restrictions (for addition, the community as a whole benefits example, battery life, camera resolution, CPU from the exchange—the roadmap can be power, and so on) of the market. In this way, altered towards the 2050 vision as the majority the environmental conditions become more of the robots advance technologically. and more challenging until they actually The technical challenge can be seen as the resemble the real conditions. technological boundary of RoboCup. The RoboCup events are very costly and by no robots have to deal with various technical chal- means comparable to a “normal” scientific lenges such as rough terrain or walking with conference. One of the issues of the RoboCup humanoids. One thing that all challenges have Federation is therefore to decrease the expens- in common, though, is that the specific

128 AI MAGAZINE Articles requirements are in fact needed in order to advance to 2050 and as yet are not part of the regular game yet. The roadmap can be changed once a technical challenge is successfully com- pleted by a number of teams. Colocated events such as IJCAI, AAMAS, DARS, and ACTUATORS underline the close relationship to the scientific community.

RoboCup Techniques and Methods As in other autonomous systems that deal with dynamic environments, a robot must have sensors in order to perceive its environment, actuators in order to manipulate its environment, as well as decision-making algorithms for the Figure 12: RoboCup@Home. selection of appropriate actions. This dynamic environment requires a fast perception of Robots deal with everyday situations such as a living room environment. They are able to open doors, follow humans, and manipulate real objects. changes (numerous cycles per second), which means that there is only little or no time for a complex processing of all the sensor data. In ther reduction of landmarks has been an - the early days, researchers in the MSL worked nounced for 2007. with ultrasound sensors, which have since The removal of the walls (which is now the been replaced by laser sensors. These days, case in all leagues) has changed the require- most teams work with methods that are based ments of perception and action, but with dif- on camera images (mostly omnidirectional fering results. With the use of walls, a strategy cameras). An important issue is the integration as used in ice hockey was useful. Alternatively, of various sensor data with the time compo- the robot could try to push the ball along the nent, which is commonly achieved using prob- walls into the opponents’ goal. In fact, the abilistic methods (for example, Kalman filters game became often stuck along the walls, since or particle filters). several players tried to push the ball in oppo- In the early days of RoboCup, perception site directions. On the other hand, the removal had to be supported by strictly defined lighting of the walls created apprehension that the conditions and colored landmarks and objects game would be often interrupted because the in all leagues. The reduction and stepwise elim- ball would leave the field. Hence the ination of these means has been primarily test- Humanoid League and the 4LL introduced speed in technical challenges. Recently, MSL, SSL, cial restart points instead of a literal throw-in. and the Humanoid League have been able to The ball is placed at a restart point inside of the achieve play within ordinary illumination sit- field, which gives the other team a small uations in indoor halls. advantage. The situation is different in the 4LL since the MSL rules require a throw-in at the place camera of the AIBO has only a limited sensibil- where the ball leaves the field. In the begin- ity. It has a visual angle of only approximately ning there were many repeated throw-ins 57 degrees in width and 42 degrees in height. because robots failed to carry this task out cor- Therefore, the robots still make use of the col- rectly. Nowadays, however, the robots have or-coded landmarks, which can actually get well developed skills, and the occurrence of confused with other objects in the background throw-ins has decreased. (for example, people’s clothing that has the In comparison, the Humanoid League had same colors as the landmarks). Teams try to no walls from the very start of the league for- avoid confusion by taking the horizon into mation. Teams were also able to use better-per- account. The position of the horizon in a par- forming cameras. Robots using omnidirection- ticular picture can be determined using the cin- al cameras could use it in the same way as the ematic chain spanning the feet to the head Middle-Size League. Other robots also benefit- with the camera, but it is subject to error given ed over the AIBOs due to the higher position of quick movements, and so on. Alternatives their cameras. include the use of the field lines, which have As far as the actuators placed on the physical already been demonstrated during the chal- robots are concerned, there are now more fast lenges between some teams, and actually a fur- and flexible maneuvers. There is a clear prefer-

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Another distinct kicking skill worth mentioning is the bicycle kick in the 4LL, which was first played by the German Team in 2002. Roughly 30 different kicks have been developed for the AIBO (for all models), ranging from powerful kicks with the head and the body to kicks with the legs in different directions. Further skills also include dribbling and running. The robots must, of course, at all times obey the rule that holding the ball for longer than three seconds is not allowed. The two-dimensional simulation league uses a simplified simulation of ball handling. The player can determine the direction and power of a kick in the kick command, while the soccer server computes the resulting speed of the ball compared to the last ball’s speed as well as the position of the ball relative to the player. Additional background noise is also added. Therefore, the setting is reasonably complicat- Figure 13: Performance during the Junior Dance Competition. ed, and good kicking and dribbling behavior are not easy to perform. Interception of a moving ball is also complicated given that percep- ence for smaller robots in MSL due to their dis- tion of the environment is challenged by noise played superiority in the games over the last interference. Varied approaches of machine years (despite the fact that the larger, heavier learning have been used to develop efficient systems are more powerful). The various kick- skills for kicking, passing, dribbling, and run- ing devices—from less successful blowing ning. A number of these skills are available in a devices to powerful spring-based devices—did library for general use. not dominate, as expected, during the games. The three-dimensional simulation league is Until 2005, the ball was played flat on the based on a physical simulation by means of ground. Since then, and especially since 2006, ODE. To date, only very simple player shapes the majority of the teams can kick the ball in are used. More realistic designs are under devel- the air, enabling the team to play more effi- opment in cooperation with the Humanoid ciently and—in terms of soccer—in more League. However, the performance of comput- humanlike fashion. This is the case for both ers is actually somewhat restricting. Again, SSL and MSL. good skills can, however, be developed using The development of the humanoid league is machine learning. still in its infancy; however, we have already A comparison between requirements for a seen rapid progress in terms of the software robot in the RoboCup environment and a and construction of humanoid robots. Only robot in a traffic scenario yields differing three years ago, walking without falling was results. Perception is easier in RoboCup considered a success. The next significant step because the robots act in a defined environ- was a goal-oriented kick. In 2006, the partici- ment. Primitive actions, such as braking, accel- pating teams made good progress in imple- erating, and steering, however, are easier in the menting key soccer skills for their robots. Over- traffic scenario. This means that reactive all, the walking ability of the robots improved. behavior is possible for the robot, and if there Walking speed increased, and walking behav- is doubt, the car can actually be stopped. In iors become more flexible and stable. Keeping robotic soccer, however, even primitive actions balance, especially in critical situations, is still have significantly more options (particularly an issue. Balance requires excellent coordina- with the legged robots) given the different tion of sensors and actuators. There is a need number of degrees of freedom (such as drib- to think about new ways of dealing with the bling with a four-legged robot). various problems, to try out new materials Cooperation between robots is still a chal- (artificial muscles, artificial skin), and to devel- lenging problem. The only league achieving op new methods in addressing the energy good results has been the Simulation League. problem. Cooperation between scientists from We can learn from this that classical planning various fields is also necessary and highly algorithms do not perform well in real time or advantageous. in dynamic environments and that the time

130 AI MAGAZINE Articles issue is critical—a robot has to perceive, to machines, neural networks, case-based reason- decide, and to act in less than 100 ms. Solu- ing, plus many others, often used in combina- tions for these kinds of problems can also be tion. They appear on different layers, starting relevant and of interest to robots in the traffic with low-level skills (such as kicking or drib- scenario. bling), including positioning as well as passing The experience in RoboCup over the last 10 behavior, and ranging up to the level of strate- years proves that the need for cooperation in gic behavior. Opponent modeling appears once robot soccer largely depends on the size of the again on the different levels and plays a key field and the number of players in a team. Sim- role for the coach competitions as discussed ilar to human soccer teams, the Simulation previously. League has a great need for high-developed Besides the Simulation Leagues in soccer and cooperation strategies. It is interesting to note, rescue, a wide range of tools used for the devel- too, that the development of these strategies opment, programming, and testing for each of followed the same path as that of human soccer the different real robot leagues exist. Many history—it started with static positions of the teams have in addition developed their own players, then the positions were dynamically tools based on simulations of their team robots adapted, and now we see increased movement in a simulated environment. Since work with combined with related complex strategies. real robots is time consuming and expensive, In the leagues of real robots, the smaller the use of such tools has many benefits. Some fields did not force long-term strategic behav- of these tools are complete simulations of the ior. Only basic cooperation skills like passing soccer games; such is the case in the 4LL. It is were used, and it seems that to a great extent possible to test different skills and strategies in they are sufficient. However, an experiment virtual reality, and of course they are also very performed with the 11 by 11 demonstration helpful for debugging. game in the Four-Legged League has shown An important utilization of such simulation that such strategies do not necessarily corre- tools is that of machine learning. Skills in the spond well across different leagues. Thus, the 4LL and in the Humanoid League need to be leagues will soon require increased cooperation optimized in a high dimensional parameter when playing larger fields, which will then space. The large degrees of freedom allow for enable the strategies already developed for the many different solutions, and the performance Simulation League to be implemented. of the robots can therefore be improved every Another difference between real and simu- year. Simulation is used for first explorations, lated robots relates to the control architecture. which later must then be evaluated and poten- Both robots use layered architectures to a large tially refined and adapted to the real robots. extent; however, while the real robots require Furthermore, a comparison of new candidates high-output efforts in order to manage both (for example, of new individuals in evolution the lower reactive control and short-term goals, or of search directions using hill climbing) with the simulated robots are able already to man- existing solutions allows for a preselection age advanced levels of control using long-term process prior to beginning the actual experi- intentions. ments. In all areas of RoboCup we have to solve In contrast, the experiments with the real optimization problems within large parameter robots lead to improvements of the simulation spaces as well as with the challenge of incom- tools, with the ability to forecast by simulation plete and questionable data. Among this, too, improving each time. Several tools are built are perception problems, such as the determi- around physical simulations resulting in a sub- nation of ball velocity or opponent modeling, stantial increase in performance. Nevertheless, primitive skills such as dash, kick, dribble, as the general experience in the RoboCup com- well as complex behavior such as team strategy munity demonstrates that simulation is still far and tactics. Machine-learning methods have away from total substitution of experiments in been tested in all of these areas and have reality. proven to be extremely valuable. The most popular programming language Machine learning has played a major role used is C++, although other languages, such as from the very beginning of RoboCup and was Prolog, are also used by some teams within the first used in the Simulation League. This league Simulation League. Even Java, a language that still provides a lot of data for machine learning would not perhaps be an obvious choice when and in addition is often used as a benchmark in talking about real-time issues and robotics, is the world outside of RoboCup. Machine-learn- used successfully in the Simulation League. ing methods include reinforcement learning, Apart from these pure programming languages, evolutionary methods, support vector other unique languages are used, such as the

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symbolic ones. Past experiences in Santos-Victor, J. 2005. RoboCup-04: Robot RoboCup have, however, clearly Soccer World Cup VIII. Lecture Notes in shown that none of these approaches Computer Science Vol. 3276. Berlin: can solve all issues alone (as is still Springer. sometimes claimed). Teams from dif- Polani D.; Browning, B.; Bonarini, A.; and ferent leagues have, for example, been Yoshida K. 2004. RoboCup-03: Robot Soccer World Cup VII. Lecture Notes in Computer able to demonstrate that high-level Science Vol. 3020. Berlin: Springer. symbolic approaches lead to advanced Kaminka, G.; Lima, P.; and Rojas, R. 2003. behavior when combined with tech- RoboCup-02: Robot Soccer World Cup VI. Lec- niques that are based on low-level ture Notes in Computer Science Vol. 2752. Please Join Us data. Higher-level approaches describe Berlin: Springer. situations qualitatively. This is also in Vancouver for Birk, A.; Coradeschi, S.; and Tadokoro, S. AAAI-07! useful in other domains like traffic 2002. RoboCup-01: Robot Soccer World Cup V. analysis or cell tracking for cancer Lecture Notes in Computer Science Vol. cells. 2377. Berlin: Springer. Most of the teams combine their Stone, P.; Balch, T.; and Kraetzschmar, G. work with other challenges from out- 2001. RoboCup-00: Robot Soccer World Cup side RoboCup in terms of addressing III. Lecture Notes in Computer Science Vol. coach language in the Simulation scientific questions as well as technical 2019. Berlin: Springer. League. solutions. The evaluation and the Veloso, M.; Pagello, E.; and Kitano, H. 2000. exchange of ideas during the competi- RoboCup-99: Robot Soccer World Cup III. Lec- tions and the symposium is an effec- ture Notes in Computer Science Vol. 1856, Conclusion tive and worthwhile foundation for p. 802. Springer, Stockholm. their future work. To achieve the 2050 Asada, M., and Kitano, H. 1999. RoboCup- RoboCup was invented to tackle 98: Robot Soccer World Cup II. Lecture Notes general AI challenges. Now, after vision—to see robots come head to head with humans in a soccer match—is not in Computer Science , Vol. 1604. Berlin: 10 years, continued development Springer. has resulted in significant in truth really as important, but it is, indeed, an inspiring goal. Kitano, H. 1998. RoboCup-97: Robot Soccer improvements particularly across World Cup I. Lecture Notes in Computer Sci- the areas of construction, percep- ence , Vol. 1395. Berlin: Springer. tion, cooperation, and interac- Acknowledgments tion. The integration of all these Ubbo Visser is an assis- We are grateful for the compilation of tant professor at the outcomes and improvements into the results from the various leagues, Center for Computing a complete running system has of which formed a substantial basis of Technologies (TZI), Uni- course presented various chal- this article. The results were compiled versity of Bremen, Ger- lenges. However, the large num- by the organizing committee members many. He holds a MSc in ber of well-performing teams now Landscape-Ecology, a of RoboCup 2006 and in particular by in existence demonstrates that a Ph.D in geoinformatics, Sven Behnke, Andreas Birk, Joschka great deal of progress has in fact and a Habilitation in Boedecker, Carlos Cardeira, Alexander been achieved during this rela- computer science. His area of expertise is AI Kleiner, Tim Laue, Paul Plöger, with the focus on knowledge representa- tively short period of time. The Thomas Röfer, and Thomas Wisspeint- tion and reasoning. The application areas actual integration of various fea- ner. In the end, however, all of this are twofold: multiagent systems (RoboCup) tures and rational behavior with work is possible only because of the and the semantic web. restricted resources is a key prob- participation of the thousands of Hans-Dieter Burkhard lem in understanding (artificial) researchers within the RoboCup com- intelligence at all. is head of the Artificial munity all over the world. Intelligence Group in Nevertheless, there is room for the Institute for Com- development with the large Technical Papers puter Science at the majority of issues in RoboCup, This article has been written as a Humboldt University of specifically relating to scientific report and therefore does not refer to Berlin. He has studied questions as well as technical any specific articles. However, all mathematics in Jena and Berlin and has worked solutions. Sometimes a gap papers presented on the RoboCup on automata theory, petri nets, distributed between the two is taken into con- symposia, listed by year of publica- sideration, separating low-level systems, VLSI diagnosis, AI, socionics, and tion, have been published as follows: cognitive robotics. He is a fellow of ECCAI features like basic perception and and vice president of the RoboCup Federa- action on the one side and high- Bredenfeld, A.; Jacoff, A.; Noda, I.; and tion. level thinking on the other. This Takahashi, Y. 2006. RoboCup-05: Robot Soc- could also be considered as the cer World Cup IX. Lecture Notes in Comput- difference between subsymbolic er Science Vol. 4020. Berlin: Springer. and statistical approaches versus Nardi, D.; Riedmiller, M.; Sammut, C.; and

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