RoboCup 2D Soccer Simulation League: Evaluation Challenges

We summarise the results of RoboCup 2D Soccer Simulation League in 2016 (Leipzig), including the main competition and the evaluation round. The evaluation round held in Leipzig confirmed the strength of RoboCup-2015 champion (WrightEagle, i.e. WE2015) in the League, with only eventual finalists of 2016 competition capable of defeating WE2015. An extended, post-Leipzig, round-robin tournament which included the top 8 teams of 2016, as well as WE2015, with over 1000 games played for each pair, placed WE2015 third behind the champion team (Gliders2016) and the runner-up (HELIOS2016). This establishes WE2015 as a stable benchmark for the 2D Simulation League. We then contrast two ranking methods and suggest two options for future evaluation challenges. The first one, "The Champions Simulation League", is proposed to include 6 previous champions, directly competing against each other in a round-robin tournament, with the view to systematically trace the advancements in the League. The second proposal, "The Global Challenge", is aimed to increase the realism of the environmental conditions during the simulated games, by simulating specific features of different participating countries.Comment: 12 pages, RoboCup-2017, Nagoya, Japan, July 201

Multilayered skill learning and movement coordination for autonomous robotic agents

With advances in technology expanding the capabilities of robots, while at the same time making robots cheaper to manufacture, robots are rapidly becoming more prevalent in both industrial and domestic settings. An increase in the number of robots, and the likely subsequent decrease in the ratio of people currently trained to directly control the robots, engenders a need for robots to be able to act autonomously. Larger numbers of robots present together provide new challenges and opportunities for developing complex autonomous robot behaviors capable of multirobot collaboration and coordination. The focus of this thesis is twofold. The first part explores applying machine learning techniques to teach simulated humanoid robots skills such as how to move or walk and manipulate objects in their environment. Learning is performed using reinforcement learning policy search methods, and layered learning methodologies are employed during the learning process in which multiple lower level skills are incrementally learned and combined with each other to develop richer higher level skills. By incrementally learning skills in layers such that new skills are learned in the presence of previously learned skills, as opposed to individually in isolation, we ensure that the learned skills will work well together and can be combined to perform complex behaviors (e.g. playing soccer). The second part of the thesis centers on developing algorithms to coordinate the movement and efforts of multiple robots working together to quickly complete tasks. These algorithms prioritize minimizing the makespan, or time for all robots to complete a task, while also attempting to avoid interference and collisions among the robots. An underlying objective of this research is to develop techniques and methodologies that allow autonomous robots to robustly interact with their environment (through skill learning) and with each other (through movement coordination) in order to perform tasks and accomplish goals asked of them. The work in this thesis is implemented and evaluated in the RoboCup 3D simulation soccer domain, and has been a key component of the UT Austin Villa team winning the RoboCup 3D simulation league world championship six out of the past seven years.Computer Science

Development of humanoid robot Aldebaran NAO's behaviour logic for soccer software

Aldebaran Roboticsi arendatud NAO humanoidrobotit kasutatakse jalgpallurina RoboCup võistlusel Standard Platform League, kus kõik robotid on sama riistvaraga ja erinevad ainult tarkvara poolest. RoboCup võistluse eesmärk on populariseerida robootikat ja intellektitehnikat. Käesoleva bakalaureusetöö eesmärk oli arendada välja RoboCup SPL 2014. aastal toimuva võistluse nõuetele vastav jalgpallitarkvara käitumisloogika, mis põhineb Texase Ülikooli võistkonna UT Austin Villa 2012. aastal avalikustatud koodil. Töö käigus uuriti Austin Villa koodi ja teiste meeskondade lahendusi, sooritati testid roboti vastupidavuse ja objektituvastuse piiride teada saamiseks ning loodi 2014. aasta võistluse reeglitele vastav käitumisstrateegia, mida on robotitel kasulik kasutada siis, kui robotitevaheline ühendus on katkenud. Loodud strateegias on robotid jagatud tsoonidesse ning kui pall on roboti tsoonis, siis lüüakse see vastase värava suunas. Kui pall ei ole mängija tsoonis, siis liigub ta vastavalt palli asukohale kindlaks määratud staatilistesse punktidesse väljakul. Töö valmis koostöös Philosopheri meeskonnaga, kes osaleb juulis 2014 Brasiilias toimuval RoboCup võistlusel. Vastavalt võistkonna eesmärkidele propageeriti robootikat Eestis ning sooritati demonstratsioone Robotexil 2013, FIRST® LEGO® League Eesti ja Läti poolfinaalis 2013 ja RoboMiku Lahingus 2014. Töö lõpus pakuti välja lahenduse idee, kuidas loodud individuaalstrateegiat muuta meeskondlikuks strateegiaks

Localization using natural landmarks off-field for robot soccer

textLocalization is an important problem that must be resolved in order for a robot to make an estimation of its location based on observation and odometry updates. Relying on artificial landmarks such as the lines, circles, and goalposts in the robot soccer domain, current robot localization requires prior knowledge and suffers from uncertainty problems due to partial observation, and thus is less generalizable compared to human beings, who refer to their surroundings for complimentary information. To improve the certainty of the localization model, we propose a framework that recognizes orientation by actively using natural landmarks from the off-field surroundings, extracting these visual features from raw images. Our approach involves identifying visual features and natural landmarks, training with localization information to understand the surroundings, and prediction based on matching of features. This approach can increase the precision of robot orientation and improve localization accuracy by eliminating uncertain hypotheses, and in addition, it is also a general approach that can be extended and applied to other localization problems as well.Computer Science

Fly with me : algorithms and methods for influencing a flock

As robots become more affordable, they will begin to exist in the world in greater quantities. Some of these robots will likely be designed to act as components in specific teams. These teams could work on tasks that are too large or complex for a single robot - or that are merely more efficiently accomplished by a team - such as surveillance in a large building or product delivery to packers in a warehouse. Multiagent systems research studies how these teams are formed and how they work together. Ad hoc teamwork, a newer area of multiagent systems research, studies how new robots can join these pre-existing teams and assist the team in accomplishing its goal. This dissertation extends and applies research in ad hoc teamwork towards the general area of flocking, which is an emergent swarm behavior. In particular, the work in this dissertation considers how ad hoc agents - called influencing agents in this dissertation - can join a flock, be recognized by the rest of the flock as part of the flock, influence the flock towards particular behaviors through their own behavior, and then separate from the flock. Specifically, the primary research question addressed in this dissertation is How can influencing agents be utilized in various types of flocks to influence the flock towards a particular behavior? In order to address this research question, this dissertation makes six main types of contributions. First, this dissertation formalizes the problem of using influencing agents to influence a flock. Second, this dissertation contributes and analyzes algorithms for influencing a flock to a desired orientation. Third, this dissertation presents methods for determining how to best add influencing agents to a flock. Fourth, this dissertation provides methods by which influencing agents can join and then leave a flock in motion. Fifth, this dissertation evaluates some of the influencing agent algorithms on a robot platform. Sixth, although the majority of this dissertation assumes the influencing agents will join a flock that behaves similarly to European starlings, this dissertation also provides insight into when and how its algorithms are generalizable to other types of flocks as well as to general teamwork and coordination research. All of the methods presented in this dissertation are empirically evaluated using a simulator that can support large flocks.Computer Science

The Alestle - Vol. 65 No. 23 - 02/21/2013

Vol. 65 No. 2

The human in the loop Perspectives and challenges for RoboCup 2050

Robotics researchers have been focusing on developing autonomous and human-like intelligent robots that are able to plan, navigate, manipulate objects, and interact with humans in both static and dynamic environments. These capabilities, however, are usually developed for direct interactions with people in controlled environments, and evaluated primarily in terms of human safety. Consequently, human-robot interaction (HRI) in scenarios with no intervention of technical personnel is under-explored. However, in the future, robots will be deployed in unstructured and unsupervised environments where they will be expected to work unsupervised on tasks which require direct interaction with humans and may not necessarily be collaborative. Developing such robots requires comparing the effectiveness and efficiency of similar design approaches and techniques. Yet, issues regarding the reproducibility of results, comparing different approaches between research groups, and creating challenging milestones to measure performance and development over time make this difficult. Here we discuss the international robotics competition called RoboCup as a benchmark for the progress and open challenges in AI and robotics development. The long term goal of RoboCup is developing a robot soccer team that can win against the world’s best human soccer team by 2050. We selected RoboCup because it requires robots to be able to play with and against humans in unstructured environments, such as uneven fields and natural lighting conditions, and it challenges the known accepted dynamics in HRI. Considering the current state of robotics technology, RoboCup’s goal opens up several open research questions to be addressed by roboticists. In this paper, we (a) summarise the current challenges in robotics by using RoboCup development as an evaluation metric, (b) discuss the state-of-the-art approaches to these challenges and how they currently apply to RoboCup, and (c) present a path for future development in the given areas to meet RoboCup’s goal of having robots play soccer against and with humans by 2050.</p