Claudio O Vilão Junior

Centro Universitário FEI · Department of Electrical Engineering

One of the challenges of Deep Learning research is to develop algorithms for mobile robotic agents that operate in uncontrolled environments, in which dynamic changes and limited processing power are common restrictions. A common solution is to develop separate vision and decision modules, so that the former is based on deep neural network architec...

This paper presents a humanoid robot framework, composed of a simulator and a telemetry interface. The framework is based on the Cross Architecture, and it is developed aiming for the RoboCup Soccer Humanoid League domain. A simulator is an important tool for testing cognitive algorithms without handling issues of real robots; furthermore, a simula...

Developing a vision system combined with a decision system for a humanoid robot, capable of playing soccer in the RoboCup domain, has been proved to be a challenging task. The computational limitations imposed by a embedded computer inside the robot and special conditions, such as the use of colored objects, led teams to use techniques based on col...

Climbing ramps is an important ability for humanoid robots: ramps exist everywhere in the world, such as in accessibility ramps and building entrances. This works proposes the use of Reinforcement Learning to learn the action policy that will make a robot walk in an upright position, in a lightly sloped terrain. The proposed architecture of our sys...

A humanoid robot capable of playing soccer needs to identify several objects in the soccer field in order to play soccer. The robot has to be able to recognize the ball, teammates and opponents, inferring information such as their distance and estimated location. In order to achieve this key requisite, this paper analyzes two descriptor algorithms,...

This paper presents a new 2D robot simulator based on the Cross Architecture for RoboCup Soccer Humanoid League domain. A simulator is an important tool for testing cognitive algorithms in robots without the need of handling with real robot problems, moreover, a simulator is extremely useful for allowing reproducibility of any developed algorithm,...

In order to perform a walk on a real environment, humanoid robots need to adapt themselves to the environment, as humans do. One approach to achieve this goal is to use Machine Learning techniques that allow robots to improve their behavior with time. In this paper, we propose a system that uses Reinforcement Learning to learn the action policy tha...

A humanoid robot capable of playing soccer needs to know where opponents and teammates are in the soccer field. The robot has to be able to recognize teammates and opponents, inferring information such as distance and estimated location of the other robots. In order to achieve this key requisite, this paper analyze two descriptor algorithms, HAAR a...

One of the goals of humanoid robot researchers is to develop a complete – in terms of hardware and software – artificial autonomous agent able to interact with humans and to act in the contemporary world, that is built for human beings. There has been an increasing number of humanoid robots in the last years, including Aldebaran’s NAO and Romeo, In...

This paper describes the design and development of a new humanoid robot named Newton, that is intended for applications in research and also to be used in the RoboCup KidSize League World Competition. Newton robot has been designed to work without any dedicated sub-controller implemented in low level hardware, often used to control the servomotors...

This paper describes a monocular vision system for humanoid robots designed and built to compete in the RoboCup Humanoid KidSize League. The proposed vision system allows the robots to track a ball, identify goals, field lines, teammates and opponents, providing information such as distances and estimated location for the robots simultaneously, usi...