The RoboCup 2018 volume presented papers focusing on the approaches of champion teams from the competitions and documents the proceedings of the RoboCup Symposium. The Symposium offers a perspective for exploring scientific and engineering principles underlying advanced robotic and AI systems.

In 2016, UT Austin Villa claimed the Standard Platform League's second place position at the RoboCup International Robot Soccer Competition in Leipzig, Germany as well as first place at both the RoboCup US Open in Brunswick, USA and the World RoboCup Conference in Beijing, China. This paper describes some of the key contributions that led to the team's victories with a primary focus on our techniques for identifying and tracking black and white soccer balls. UT Austin Villa's ball detection system was overhauled in order to transition from the league's bright orange ball, used every year of the competition prior to 2016, to the truncated icosahedral pattern commonly associated with soccer balls. We evaluated and applied a series of heuristic region-of-interest identification techniques and supervised machine learning methods to produce a ball detector capable of reliably detecting the ball's position with no prior knowledge of the ball's position. In 2016, UT Austin Villa suffered only a single loss which occurred after regulation time during a penalty kick shootout. We attribute much of UT Austin Villa's success in 2016 to our robots' effectiveness at quickly and consistently localizing the ball. In this work we discuss the specifics of UT Austin Villa's ball detector implementation which are applicable to the specific problem of ball detection in RoboCup, as well as to the more general problem of fast and precise object detection in computationally constrained domains. Furthermore we provide empirical analyses of our approach to support the conclusion that modern deep learning techniques can enhance visual recognition tasks even in the face of these computational constraints.

The Standard Platform League is one of the main competitions at the annual RoboCup world championships. In this competition, teams of five humanoid robots play soccer against each other. In 2013, the league began a new competition which serves as a testbed for cooperation without pre-coordination: the Drop-in Player Competition. Instead of homogeneous robot teams that are each programmed by the same people and hence implicitly pre-coordinated, this competition features ad hoc teams, i.e. teams that consist of robots originating from different RoboCup teams and as such running different software. In the article advertised by this extended abstract, we provide an overview of this competition, including its motivation, rules, and how these rules have changed across three iterations of the competition. We also present and analyze the strategies utilized by various drop-in players as well as the results of the first three competitions. The article concludes by suggesting improvements for future competitive evaluations of ad hoc teamwork. To the best of our knowledge, the three Drop-in Player Competitions described in the article are the largest annual ad hoc teamwork robotic experiment to date. Across three years, the competition saw 56 entries from 30 different organizations and consisted of 510 minutes of game time that resulted in approximately 85 robot hours.

Each individual bird in a flock of birds updates its behavior based on the behaviors of its neighbors. Previous work has considered how a small set of algorithmically controlled influencing agents, or robot birds, can influence the flock to behave in a particular way - such as to avoid airports or wind farms. These robot birds are assumed to be seen by the flock as ordinary birds, and hence are able to influence their neighbors. However, we are aware of no previous work that has considered the issues related to robot birds joining and leaving flocks of natural birds. Due to the influence the robot birds have on the flock as soon as members of the flock become neighbors, joining and leaving are not straight-forward. In this abstract, we discuss simple approaches for robot birds to use when joining and leaving flocks of natural birds.

This paper describes the research focus and ideas incorporated in the UT Austin Villa Standard Platform league team entering the RoboCup competition in 2016. UT Austin Villa is a team representing the Department of Computer Science at The University of Texas at Austin.

UT Austin Villa is a robot soccer team that has competed in the annual RoboCup soccer competitions since 2003. The team has won several championships and has inspired research contributions spanning many topics in robotics and artificial intelligence. This article summarizes some of these research contributions and provides a snapshot into the current development status of the team. Educational uses of the team's code bases are also presented.

Many different animals, including birds and fish, exhibit a collective behavior known as flocking. Flocking behavior is believed by biologists to emerge from relatively simple local control rules utilized by each individual in a flock. Specifically, each individual adjusts its behavior based on the behaviors of its closest neighbors. In our work we consider the possibility of adding a small set of influencing agents, which are under our control, to a flock. Specifically, we advance existing work on adding in fluencing agents into a flock and begin to consider the case in which in fluencing agents must join a flock in motion. Following ad hoc teamwork methodology, we assume that we are given knowledge of, but no direct control over, the rest of the flock. As such, we use the in fluencing agents to alter the flock's behavior - for example by encouraging all of the individuals to face the same direction or by altering the trajectory of the flock. In this paper we define several new methods for adding influencing agents into the flock and compare them against existing methods.

Ad hoc teamwork refers to the challenge of designing agents that can influence the behavior of a team, without prior coordination with its teammates. This paper considers influencing a flock of simple robotic agents to adopt a desired behavior within the context of ad hoc teamwork. Specifically, we examine how the ad hoc agents should behave in order to orient a flock towards a target heading as quickly as possible when given knowledge of, but no direct control over, the behavior of the flock. We introduce three algorithms which the ad hoc agents can use to influence the flock, and we examine the relative importance of coordinating the ad hoc agents versus planning farther ahead when given fixed computational resources. We present detailed experimental results for each of these algorithms, concluding that in this setting, inter-agent coordination and deeper lookahead planning are no more beneficial than short-term lookahead planning.

The Standard Platform League is one of the main competitions of the annual RoboCup world championships. In this competition, teams of five humanoid robots play soccer against each other. In 2014, the league added a new sub-competition which serves as a testbed for cooperation without pre-coordination: the Drop-in Player Competition. Instead of homogeneous robot teams that are each programmed by the same people and hence implicitly pre-coordinated, this competition features ad hoc teams, i.e. teams that consist of robots originating from different RoboCup teams and that are each running different software. In this paper, we provide an overview of this competition, including its motivation and rules. We then present and analyze the results of the 2014 competition, which gathered robots from 23 teams, involved at least 50 human participants, and consisted of fifteen 20-minute games for a total playing time of 300 minutes. We also suggest improvements for future iterations, many of which will be evaluated at RoboCup 2015.

Flocking is a emergent behavior exhibited by many different animal species, including birds and fish. In our work we consider adding a small set of influencing agents, that are under our control, into a flock. Following ad hoc teamwork methodology, we assume that we are given knowledge of, but no direct control over, the rest of the flock. In our ongoing work highlighted in this abstract, we are specifically considering the problem of where to initially place influencing agents that we add to such a flock. We use these influencing agents to influence the flock to behave in a particular way - for example, to fly in a particular orientation or fly in a particular pattern such as to avoid an obstacle.

Flocking is a fascinating collective behavior exhibited by many different animals including birds and fish. As understood by biologists, the overall flocking behavior emerges from relatively simple local control rules by which each individual adjusts its own trajectory based on those of its closest neighbors. We consider the possibility of adding a small set of influencing agents, that are under our control, into a flock. Specifically, in this paper we consider where in the flock to place the influencing agents that we add to the flock. Following ad hoc teamwork methodology, we assume that we are given knowledge of, but no direct control over, the rest of the flock. We use the influencing agents to alter the flock's trajectory, for instance to avoid an obstacle. We define several methodologies for placing the influencing agents into the flock, and compare them via detailed experimental results.

The Standard Platform League is a soccer league at the annual RoboCup world championships in which teams of five humanoid robots play against each other. In 2014, the Dropin Player Competition was added to the league to serve as a testbed for cooperation without pre-coordination. Instead of homogeneous robot teams that are programmed by each team to implicitly work together, this competition features ad hoc teams, i.e. teams that consist of robots originating from different RoboCup teams and that are each running different software. In this extended abstract, we provide an overview of this competition, including its motivation and rules.

As the prevalence of autonomous agents grows, so does the number of interactions between these agents. Therefore, it is desirable for these agents to be capable of banding together with previously unknown teammates towards a common goal: to collaborate without pre-coordination. While past research on ad hoc teamwork has focused mainly on theoretical treatments and empirical studies in relatively simple domains, the long-term vision has been to enable robots and other autonomous agents to exhibit the sort of flexibility and adaptability on complex tasks that people do, for example when they play games of "pick-up" basketball or soccer. This paper introduces a series of pick-up robot soccer experiments that were carried out in three different leagues at the international RoboCup competition in 2013. In all cases, agents from different labs were put on teams with no pre-coordination. This paper introduces the structure of these experiments, describes the strategies used by UT Austin Villa in each challenge, and analyzes the results. The paper's main contribution is the introduction of a new large-scale ad hoc teamwork testbed that can serve as a starting point for future experimental ad hoc teamwork research.

first “Drop-in Challenge” games that was held at RoboCup 2013 in Eindhoven, The Netherlands. Typically, RoboCup soccer games involve a team of robots programmed by one university against a team programmed by another. As such, the teamwork strategies can all be “programmed in.” However, as robots and their agents become more capable of long-term autonomy, there will be increasing opportunities and need for “ad hoc teamwork” in which agents need to cooperate without prior coordination. The drop-in challenge at RoboCup provides an opportunity to study ad hoc teamwork by randomly selecting different RoboCup teams to each contribute one robot to a team that plays against another such team. The robots must be programmed to work with previously unknown teammates.

In 2012, UT Austin Villa claimed the Standard Platform League championships at both the US Open and the 2012 RoboCup competition held in Mexico City. This paper describes the code release associated with the team and discusses the key contributions of the release. This release will enable teams entering the Standard Platform League and researchers using the Naos to have a solid foundation from which to start their work as well as providing useful modules to existing researchers and RoboCup teams. We expect it to be of particular interest because it includes the architecture, logic modules, and debugging tools that led to the team’s success in 2012. This architecture is designed to be flexible and robust while enabling easy testing and debugging of code. The vision code was designed for easy use in creating color tables and debugging problems. A custom localization simulator that is included permits fast testing of full team scenarios. Also included is the kick engine which runs through a number of static joint poses and adapts them to the current location of the ball. This code release will provide a solid foundation for new RoboCup teams and for researchers that use the Naos.

As the prevalence of autonomous agents grows, so does the number of interactions between these agents. Therefore, it is desirable for these agents to be capable of collaborating without pre-coordination. While past research on ad hoc teamwork has focused mainly on relatively simple domains, the long-term vision has been to enable robots and other autonomous agents to exhibit the sort of flexibility and adaptability on complex tasks that people do. This research introduces a series of pick-up robot soccer experiments that were carried out in three different leagues at the international RoboCup competition in 2013. In all cases, agents from different labs were put on teams with no pre-coordination. This abstract summarizes the structure of these experiments and analyzes the results. The work describes a new large-scale ad hoc teamwork testbed that can serve as a starting point for future experimental ad hoc teamwork research.

The Standard Platform League is one of the competitions of the annual RoboCup world championships. In this competition, teams of five humanoid robots play soccer against each other. In 2014, the league added a new sub-competition which serves as a testbed for cooperation without pre-coordination: The Drop-in Player Competition. Instead of homogeneous robot teams that are each programmed by the same people and hence implicitly pre-coordinated, this competition features ad hoc teams, i. e. teams that consist of robots originating from different RoboCup teams and that are each running different software. In this paper, we provide an overview of this competition, including its motivation, current rules, and