267 research outputs found

    FC Portugal - High-Level Skills Within A Multi-Agent Environment

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    Ao longo dos anos a RoboCup, uma competição internacional de robótica e da inteligência artificia, foi palco de muitos desenvolvimentos e melhorias nestes duas áreas científicas. Esta competição tem diferentes desafios, incluindo uma liga de simulação 3D (Simulation 3D League). Anualmente, ocorre um torneio de jogos de futebol simulados entre as várias equipas participantes na Simulation 3D League, todas estas equipas deveram ser compostas por 11 robôs humanoides. Esta simulação obedece às leis da física de modo a se aproximar das circunstâncias dos jogos reais. Além disso, as regras da competição são semelhantes às regras originais do futebol com algumas alterações e adaptações. A equipa portuguesa, o FC Portugal 3D é um participante assíduo nos torneios desta liga e chegou até a ser vitoriosa várias vezes nos últimos anos, no entanto, para participar nesta competição é necessário que as equipas tenham os seus agentes capazes de executar skills (ou habilidades) de baixo nível como andar, chutar e levantar-se. O bom registo da equipa FC Portugal 3D advém do facto de os métodos utilizados para treinar os seus jogadores serem continuamente melhorados resultando em melhores habilidades. De facto, considera-se que estes comportamentos de baixo nível estão num ponto em que é possível mudar o foco das implementações para competências de alto nível que deveram ser baseadas nestas competências fundamentais de baixo nível. O futebol pode ser visto como um jogo cooperativo onde jogadores da mesma equipa têm de trabalhar em conjunto para vencer os seus adversários, consequentemente, este jogo é considerado como um bom ambiente para desenvolver, testar e aplicar implementações relativas a cooperações multi-agente. Com isto em mente, o objetivo desta dissertação é construir uma setplay multi-agente baseada nas skills de baixo nível previamente implementadas pela FC Portugal para serem usadas em situações de jogo específicas em que a intenção principal é marcar um golo. Recentemente, muitos participantes da 3D League (incluindo a equipa portuguesa) têm desenvolvido competências utilizando métodos de Deep Reinforcement Learning obtendo resultados satisfatórios num tempo razoável. A abordagem adotada neste projeto foi a de utilizar o algoritmo de Reinforcement Learning, PPO, para treinar todos os ambientes criados com o intuito de desenvolver a setplay pretendida, os resultados dos treinos estão presentes no penúltimo capítulo deste documento seguidos de sugestões para implementações futuras.Throughout the years the RoboCup, an international competition of robotics and artificial intelligence, saw many developments and improvements in these scientific fields. This competition has different types of challenges including a 3D Simulation League that has an annual tournament of simulated soccer games played between several teams each composed of 11 simulated humanoid robots. The simulation obeys the laws of physics in order to approximate the games as much as possible to real circumstances, in addition, the rules are similar to the original soccer rules with a few alterations and adaptations. The Portuguese team, FC Portugal 3D has been an assiduous participant in this league tournaments and was even victorious several times in the past years, nonetheless, to participate in this competition is necessary for teams to have their agents able to execute low-level skills such as walk, kick and get up. The good record of the FC Portugal 3D team comes from the fact that the methods used to train the robots keep being improved, resulting in better skills. As a manner of fact, it is considered that these low-level behaviors are at a point that is possible to shift the implementations' focus to high-level skills based on these fundamental low-level skills. Soccer can be seen as a cooperative game where players from the same team have to work together to beat their opponents, consequently, this game is considered to be a good environment to develop, test, and apply cooperative multi-agent implementations. With this in mind, the objective of this dissertation is to construct a multi-agent setplay based on FC Portugal's low-level skills to be used in certain game situations where the main intent is to score a goal. Recently, many 3D League participants (including the Portuguese team) have been developing skills using Deep Learning methods and obtaining successful results in a reasonable time. The approach taken on this project was to use the Reinforcement Learning algorithm PPO to train all the environments that were created to develop the intended setplay, the results of the training are present in the second-to-last chapter of this document followed by suggestions for future implementations

    Multi-Agent Task Allocation for Robot Soccer

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    This is the published version. Copyright De GruyterThis paper models and analyzes task allocation methodologies for multiagent systems. The evaluation process was implemented as a collection of simulated soccer matches. A soccer-simulation software package was used as the test-bed as it provided the necessary features for implementing and testing the methodologies. The methodologies were tested through competitions with a number of available soccer strategies. Soccer game scores, communication, robustness, fault-tolerance, and replanning capabilities were the parameters used as the evaluation criteria for the mul1i-agent systems

    Humanoid Robot Kick in Motion Ability for Playing Robotic Soccer

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    Robotics and Artificial Intelligence are two deeply intertwined fields of study, currently experiencing formidable growth. To foster these developments, the RoboCup initiative is a fantastic test bed to experiment new approaches. This dissertation seeks to gather these possibilities to design and implement a humanoid robotic kick system employing deep neural networks, capable of fluidly kicking a ball while walking. This dissertation's work is rooted in the groundwork laid by previous FCPortugal3D teams so as to take the existing algorithms and skills into its consideration. In this way, a transition between a dynamic movement situation and one where the agent is kicking is achieved. Furthermore, it uses the new agent framework developed by the FCPortugal3D team so as to allow these tests to be built upon for future situations with ease

    Microsoft robotics soccer challenge : movement optimization of a quadruped robot

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    Estágio realizado na Universidade de Aveiro e orientado pelo Prof. Doutor Nuno LauTese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

    RoboCup@Home: Analysis and results of evolving competitions for domestic and service robots

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    Scientific competitions are becoming more common in many research areas of artificial intelligence and robotics, since they provide a shared testbed for comparing different solutions and enable the exchange of research results. Moreover, they are interesting for general audiences and industries. Currently, many major research areas in artificial intelligence and robotics are organizing multiple-year competitions that are typically associated with scientific conferences. One important aspect of such competitions is that they are organized for many years. This introduces a temporal evolution that is interesting to analyze. However, the problem of evaluating a competition over many years remains unaddressed. We believe that this issue is critical to properly fuel changes over the years and measure the results of these decisions. Therefore, this article focuses on the analysis and the results of evolving competitions. In this article, we present the RoboCup@Home competition, which is the largest worldwide competition for domestic service robots, and evaluate its progress over the past seven years. We show how the definition of a proper scoring system allows for desired functionalities to be related to tasks and how the resulting analysis fuels subsequent changes to achieve general and robust solutions implemented by the teams. Our results show not only the steadily increasing complexity of the tasks that RoboCup@Home robots can solve but also the increased performance for all of the functionalities addressed in the competition. We believe that the methodology used in RoboCup@Home for evaluating competition advances and for stimulating changes can be applied and extended to other robotic competitions as well as to multi-year research projects involving Artificial Intelligence and Robotics

    Recognizing Teamwork Activity In Observations Of Embodied Agents

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    This thesis presents contributions to the theory and practice of team activity recognition. A particular focus of our work was to improve our ability to collect and label representative samples, thus making the team activity recognition more efficient. A second focus of our work is improving the robustness of the recognition process in the presence of noisy and distorted data. The main contributions of this thesis are as follows: We developed a software tool, the Teamwork Scenario Editor (TSE), for the acquisition, segmentation and labeling of teamwork data. Using the TSE we acquired a corpus of labeled team actions both from synthetic and real world sources. We developed an approach through which representations of idealized team actions can be acquired in form of Hidden Markov Models which are trained using a small set of representative examples segmented and labeled with the TSE. We developed set of team-oriented feature functions, which extract discrete features from the high-dimensional continuous data. The features were chosen such that they mimic the features used by humans when recognizing teamwork actions. We developed a technique to recognize the likely roles played by agents in teams even before the team action was recognized. Through experimental studies we show that the feature functions and role recognition module significantly increase the recognition accuracy, while allowing arbitrary shuffled inputs and noisy data
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