1,070 research outputs found
Making High-Performance Robots Safe and Easy to Use for an Introduction to Computing
Robots are a popular platform for introducing computing and artificial
intelligence to novice programmers. However, programming state-of-the-art
robots is very challenging, and requires knowledge of concurrency, operation
safety, and software engineering skills, which can take years to teach. In this
paper, we present an approach to introducing computing that allows students to
safely and easily program high-performance robots. We develop a platform for
students to program RoboCup Small Size League robots using JavaScript. The
platform 1) ensures physical safety at several levels of abstraction, 2) allows
students to program robots using the JavaScript in the browser, without the
need to install software, and 3) presents a simplified JavaScript semantics
that shields students from confusing language features. We discuss our
experience running a week-long workshop using this platform, and analyze over
3,000 student-written program revisions to provide empirical evidence that our
approach does help students.Comment: 8 pages, 7 figures, 4 table
State and prospects of development of team interaction of robots on the example of competitions of the world tournament "Robocup"
Today, effective group work management is one of the main problems of mechatronics. As the development of generalized algorithms and principles of management is at an early level, the scientific community has formed several model tasks, one of which reads as follows: "By the middle of the XXI century the winner of the last world championship”. As part of the wording, the world's first RoboCup competition was launched in 1996 to promote research in the field of robot design and artificial intelligence.
The main task of the article is to analyze and highlight the current state of algorithms for command control of robots on the example of the RoboCup world tournament. The article describes the general schemes of team interaction in the divisions of the tournament, the hardware characteristics of the agents, the history, chronological development and the current state of the rules of the divisions. Based on the analysis, a comparative table of basic technical parameters of RoboCup leagues and approaches used for team management is formed. The conclusion concerning the most actual directions of researches of methods of group interaction is made
FC Portugal 3D Simulation Team: Team Description Paper 2020
The FC Portugal 3D team is developed upon the structure of our previous
Simulation league 2D/3D teams and our standard platform league team. Our
research concerning the robot low-level skills is focused on developing
behaviors that may be applied on real robots with minimal adaptation using
model-based approaches. Our research on high-level soccer coordination
methodologies and team playing is mainly focused on the adaptation of
previously developed methodologies from our 2D soccer teams to the 3D humanoid
environment and on creating new coordination methodologies based on the
previously developed ones. The research-oriented development of our team has
been pushing it to be one of the most competitive over the years (World
champion in 2000 and Coach Champion in 2002, European champion in 2000 and
2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation
and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and
European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes
some of the main innovations of our 3D simulation league team during the last
years. A new generic framework for reinforcement learning tasks has also been
developed. The current research is focused on improving the above-mentioned
framework by developing new learning algorithms to optimize low-level skills,
such as running and sprinting. We are also trying to increase student contact
by providing reinforcement learning assignments to be completed using our new
framework, which exposes a simple interface without sharing low-level
implementation details
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
Boosting Studies of Multi-Agent Reinforcement Learning on Google Research Football Environment: the Past, Present, and Future
Even though Google Research Football (GRF) was initially benchmarked and
studied as a single-agent environment in its original paper, recent years have
witnessed an increasing focus on its multi-agent nature by researchers
utilizing it as a testbed for Multi-Agent Reinforcement Learning (MARL).
However, the absence of standardized environment settings and unified
evaluation metrics for multi-agent scenarios hampers the consistent
understanding of various studies. Furthermore, the challenging 5-vs-5 and
11-vs-11 full-game scenarios have received limited thorough examination due to
their substantial training complexities. To address these gaps, this paper
extends the original environment by not only standardizing the environment
settings and benchmarking cooperative learning algorithms across different
scenarios, including the most challenging full-game scenarios, but also by
discussing approaches to enhance football AI from diverse perspectives and
introducing related research tools. Specifically, we provide a distributed and
asynchronous population-based self-play framework with diverse pre-trained
policies for faster training, two football-specific analytical tools for deeper
investigation, and an online leaderboard for broader evaluation. The overall
expectation of this work is to advance the study of Multi-Agent Reinforcement
Learning on Google Research Football environment, with the ultimate goal of
benefiting real-world sports beyond virtual games
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Hypernetworks Analysis of RoboCup Interactions
Robotic soccer simulations are controlled environments in which the rich variety of interactions among agents make them good candidates to be studied as complex adaptive systems. The challenge is to create an autonomous team of soccer agents that can adapt and improve its behaviour as it plays other teams. By analogy with chess, the movements of the soccer agents and the ball form ever-changing networks as players in one team form structures that give their team an advantage. For example, the Defender’s Dilemma involves relationships between an attacker with the ball, a team-mate and a defender. The defender must choose between tackling the player with the ball, or taking a position to intercept a pass to the other attacker. Since these structures involve more that two interacting entities it is necessary to go beyond networks to multidimensional hypernetworks. In this context, this thesis investigates (i) is it possible to identify patterns of play, that lead a team to obtain an advantage ?, (ii) is it possible to forecast with a good degree of accuracy if a certain game action or sequence of game actions is going to be successful, before it has been completed ?, and (iii) is it possible to make behavioural patterns emerge in the game without specifying the behavioural rules in detail ? To investigate these research questions we devised two methods to analyse the interactions between robotic players, one based on traditional programming and one based on Deep Learning. The first method identified thousands of Defender’s Dilemma configurations from RoboCup 2D simulator games and found a statistically significant association between winning and the creation of the defender’s dilemma by the attackers of the winning team. The second method showed that a feedforward Artificial Neural Network trained on thousands of games can take as input the current game configuration and forecast to a high degree of accuracy if the current action will end up in a goal or not. Finally, we designed our own fast and simple robotic soccer simulator for investigating Reinforcement Learning. This showed that Reinforcement Learning using Proximal Policy Optimization could train two agents in the task of scoring a goal, using only basic actions without using pre-built hand-programmed skills. These experiments provide evidence that it is possible: to identify advantageous patterns of play; to forecast if an action or sequence of actions will be successful; and to make behavioural patterns emerge in the game without specifying the behavioural rules in detail
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