Benchmarking robot cooperation without pre-coordination in the RoboCup Standard Platform League drop-in player competition

Abstract — 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. I

Fear Learning for Flexible Decision Making in RoboCup: A Discussion

In this paper, we address the stagnation of RoboCup com- petitions in the fields of contextual perception, real-time adaptation and flexible decision-making, mainly in regards to the Standard Platform League (SPL). We argue that our Situation-Aware FEar Learning (SAFEL) model has the necessary tools to leverage the SPL competition in these fields of research, by allowing robot players to learn the behaviour profile of the opponent team at runtime. Later, players can use this knowledge to predict when an undesirable outcome is imminent, thus having the chance to act towards preventing it. We discuss specific scenarios where SAFEL’s associative learning could help to increase the positive outcomes of a team during a soccer match by means of contextual adaptation

Abstracting Multidimensional Concepts for Multilevel Decision Making in Multirobot Systems

Multirobot control architectures often require robotic tasks to be well defined before allocation. In complex missions, it is often difficult to decompose an objective into a set of well defined tasks; human operators generate a simplified representation based on experience and estimation. The result is a set of robot roles, which are not best suited to accomplishing those objectives. This thesis presents an alternative approach to generating multirobot control algorithms using task abstraction. By carefully analysing data recorded from similar systems a multidimensional and multilevel representation of the mission can be abstracted, which can be subsequently converted into a robotic controller. This work, which focuses on the control of a team of robots to play the complex game of football, is divided into three sections: In the first section we investigate the use of spatial structures in team games. Experimental results show that cooperative teams beat groups of individuals when competing for space and that controlling space is important in the game of robot football. In the second section, we generate a multilevel representation of robot football based on spatial structures measured in recorded matches. By differentiating between spatial configurations appearing in desirable and undesirable situations, we can abstract a strategy composed of the more desirable structures. In the third section, five partial strategies are generated, based on the abstracted structures, and a suitable controller is devised. A set of experiments shows the success of the method in reproducing those key structures in a multirobot system. Finally, we compile our methods into a formal architecture for task abstraction and control. The thesis concludes that generating multirobot control algorithms using task abstraction is appropriate for problems which are complex, weakly-defined, multilevel, dynamic, competitive, unpredictable, and which display emergent properties

Arquitetura do agente da equipa de futebol robótico CAMBADA

Mestrado em Engenharia Electrónica e de TelecomunicaçõesThe software agent is the process where all the Artificial Intelligence resides and is responsible for taking high-level decisions. CAMBADA is the robotics soccer team of the IRIS research group, from IEETA, University of Aveiro, that participates in the Middle-Size League of RoboCup. Robotics is an emerging multidisciplinary area that joins computer science, electronics and mechanics and this thesis includes an overview on the general architecture of the CAMBADA robots, from hardware to software, over which all the presented work has been developed. In the competitions context, the reasoning capabilities define the success or the failure of a team. Given the high dynamism of the games, it becomes vital to take the correct decisions, at the right time and in a collaborative way. This thesis intends to improve the structure of the agent, from the code organization to the actual software architecture. A new behavior management model was developed and adopted for the competitions. The constant evolution of the Middle-Size League pushes teams to adapt to new rules each new year. In this context, some novel behaviors were developed and others have been refined in the new architecture. Moreover, for the creation, test and validation of these behaviors, the creation of a series of applications was needed for development, calibration and debugging. The new agent architecture provided a faster and more robust behavior development, and the improvements made on behaviours led to a better global performance of the team in the competitions.O agente de software e o processo onde reside toda a componente de Inteligência Artificial, responsável por tomar as decisões de alto nível. CAMBADA é a equipa de futebol robótico do grupo de investigação IRIS, da unidade de investigação IEETA, da Universidade de Aveiro que participa na Liga dos Robôs Médios do RoboCup. A robótica é uma área multidisciplinar emergente que junta ciências da computação, eletrónica e mecânica e nesta tese está incluída uma explicação geral sobre a arquitetura dos robôs CAMBADA, desde o hardware ao software, sobre os quais foi desenvolvido todo o trabalho apresentado. No contexto de competição, a capacidade de raciocínio é o que define o sucesso ou o insucesso das equipas. Dado o dinamismo atual dos jogos, torna-se vital tomar as decisões corretas, no momento certo e em equipa. Com esta tese pretende melhorar-se a estrutura do agente, desde a organização do código a própria arquitetura de software. Um novo modelo de gestão de comportamentos foi desenvolvido e adotado para as competições. A constante evolução da Liga de Robôs Médios leva as equipas a terem de se adaptar a novas regras todos os anos. Neste contexto, alguns comportamentos foram desenvolvidos de raíz e outros foram melhorados na nova arquitetura. No entanto, para a criação, teste e validação destes comportamentos foi necessária a criação de aplicações de suporte ao desenvolvimento, calibração e de depuração. A nova arquitetura permitiu um desenvolvimento mais rápido e robusto de comportamentos, e os avanços nos comportamentos levaram a uma melhoria considerável no desempenho global da equipa em termos competitivos

SAFEL - A Situation-aware Fear Learning Model

This thesis proposes a novel and robust online adaptation mechanism for threat prediction and prevention capable of taking into consideration complex contextual and temporal information in its internal learning processes. The proposed mechanism is a hybrid cognitive computational model named SAFEL (Situation-Aware FEar Learning), which integrates machine learning algorithms with concepts of situation-awareness from expert systems to simulate both the cued and contextual fear-conditioning phenomena. SAFEL is inspired by well-known neuroscience findings on the brain's mechanisms of fear learning and memory to provide autonomous robots with the ability to predict undesirable or threatening situations to themselves. SAFEL's ultimate goal is to allow autonomous robots to perceive intricate elements and relationships in their environment, learn with experience through autonomous environmental exploration, and adapt at execution time to environmental changes and threats. SAFEL consists of a hybrid architecture composed of three modules, each based on a different approach and inspired by a different region (or function) of the brain involved in fear learning. These modules are: the Amygdala Module (AM), the Hippocampus Module (HM) and the Working Memory Module (WMM). The AM learns and detects environmental threats while the HM makes sense of the robot's context. The WMM is responsible for combining and associating the two types of information processed by the AM and HM. More specifically, the AM simulates the cued conditioning phenomenon by creating associations between co-occurring aversive and neutral environmental stimuli. The AM represents the kernel of emotional appraisal and threat detection in SAFEL's architecture. The HM, in turn, handles environmental information at a higher level of abstraction and complexity than the AM, which depicts the robot's situation as a whole. The information managed by the HM embeds in a unified representation the temporal interactions of multiple stimuli in the environment. Finally, the WMM simulates the contextual conditioning phenomenon by creating associations between the contextual memory formed in the HM and the emotional memory formed in the AM, thus giving emotional meaning to the contextual information acquired in past experiences. Ultimately, any previously experienced pattern of contextual information triggers the retrieval of that stored contextual memory and its emotional meaning from the WMM, warning the robot that an undesirable situation is likely to happen in the near future. The main contribution of this work as compared to the state of the art is a domain-independent mechanism for online learning and adaptation that combines a fear-learning model with the concept of temporal context and is focused on real-world applications for autonomous robotics. SAFEL successfully integrates a symbolic rule-based paradigm for situation management with machine learning algorithms for memorizing and predicting environmental threats to the robot based on complex temporal context. SAFEL has been evaluated in several experiments, which analysed the performance of each module separately. Ultimately, we conducted a comprehensive case study in the robot soccer scenario to evaluate the collective work of all modules as a whole. This case study also analyses to which extent the emotional feedback of SAFEL can improve the intelligent behaviour of a robot in a practical real-world situation, where adaptive skills and fast/flexible decision-making are crucial

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions

Welcome to ROBOTICA 2009. This is the 9th edition of the conference on Autonomous Robot Systems and Competitions, the third time with IEEE‐Robotics and Automation Society Technical Co‐Sponsorship. Previous editions were held since 2001 in Guimarães, Aveiro, Porto, Lisboa, Coimbra and Algarve. ROBOTICA 2009 is held on the 7th May, 2009, in Castelo Branco , Portugal. ROBOTICA has received 32 paper submissions, from 10 countries, in South America, Asia and Europe. To evaluate each submission, three reviews by paper were performed by the international program committee. 23 papers were published in the proceedings and presented at the conference. Of these, 14 papers were selected for oral presentation and 9 papers were selected for poster presentation. The global acceptance ratio was 72%. After the conference, eighth papers will be published in the Portuguese journal Robótica, and the best student paper will be published in IEEE Multidisciplinary Engineering Education Magazine. Three prizes will be awarded in the conference for: the best conference paper, the best student paper and the best presentation. The last two, sponsored by the IEEE Education Society ‐ Student Activities Committee. We would like to express our thanks to all participants. First of all to the authors, whose quality work is the essence of this conference. Next, to all the members of the international program committee and reviewers, who helped us with their expertise and valuable time. We would also like to deeply thank the invited speaker, Jean Paul Laumond, LAAS‐CNRS France, for their excellent contribution in the field of humanoid robots. Finally, a word of appreciation for the hard work of the secretariat and volunteers. Our deep gratitude goes to the Scientific Organisations that kindly agreed to sponsor the Conference, and made it come true. We look forward to seeing more results of R&D work on Robotics at ROBOTICA 2010, somewhere in Portugal