Thiago Pedro Donadon Homem

Federal Institute of São Paulo | IFSP · Informática

This paper investigates the use of Deep Reinforcement Learning (DRL) applied to the humanoid robot soccer environment, where a robot must learn from basic to complex skills while it interacts with the environment through images received by its own camera. To do so, the Dueling Double DQN algorithm is used: it receives the images from the robot's ca...

O artigo apresenta o projeto de extensão realizado no campus IFSP-Pirituba denominado “Empoderamento Feminino através da geração de renda pelas vendas”. A proposta do projeto era atender um grupo de 25 mulheres acima de 16 anos com pouca ou nenhuma escolaridade em condições de vulnerabilidade social para capacitação profissional através do curso de...

Object detection techniques that achieve state-of-the-art detection accuracy employ convolutional neural networks, implemented to have lower latency in graphics processing units. Some hardware systems, such as mobile robots, operate under constrained hardware situations, but still benefit from object detection capabilities. Multiple network models...

En el contexto de la pandemia actual, esta investigación crea una aplicación web que permite monitorear el uso de la mascarilla protectora en ambientes públicos. Utilizando el framework Flask, en el lenguaje de Python, la aplicación cuenta con un panel de control que ayuda a visualizar los datos obtenidos. El proceso de detección utiliza el algorit...

Devido ao crescimento de casos de COVID-19 no ano de 2020, o Município de São Paulo sancionou o decreto 59396, tornando obrigatório o uso de máscaras em estabelecimentos públicos, conforme a orientação da Organização Mundial de Saúde (OMS), a fim de diminuir o contágio da doença. Neste sentido, este trabalho implementa um sistema web que permite o...

A navegação de robôs móveis em ambiente real é um das tarefas mais complexas em robótica, O robô deve se movimentar pelo ambiente, desviando dos obstáculos e chegar em um ponto alvo, sem qualquer intervenção humana. Este trabalho investiga e implementa o algoritmo Q-learning em simulador e em robô real. Como contribuição, a integração de ações do r...

Object detection techniques that achieve state-of-the-art detection accuracy employ convolutional neural networks, implemented to have optimal performance in graphics processing units. Some hardware systems, such as mobile robots, operate under constrained hardware situations, but still benefit from object detection capabilities. Multiple network m...

Esse relato de experiência apresenta ações estratégicas de interatividade e interação online para o ensino e aprendizagem por meio das tecnologias digitais de informação e comunicação (TDICs), dentro da percepção de um ambiente virtual para construção do conhecimento colaborativo em cenário de isolamento presencial decorrente da COVID-19. O objetiv...

The development of autonomous agents that perform tasks with the same dexterity as performed by humans is one of the challenges of artificial intelligence and robotics. This motivates the research on intelligent agents, since the agent must choose the best action in a dynamic environment in order to maximise the final score. In this context, the pr...

Recent advances in deep learning point towards the use of computer vision systems based on Deep Neural Networks (DNNs). However, these network architectures are optimized to be executed in specialized hardware, such as in computers with Graphics Processing Units (GPU). Such hardware is rarely available in embedded computers, for instance, those use...

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...

This paper proposes a new Case-Based Reasoning (CBR) approach, named Q-CBR, that uses Qualitative Spatial Reasoning theory to model, retrieve and reuse cases by means of spatial relations. Qualitative relations between objects, represented in terms of the EOPRA formalism, are stored as qualitative cases that are applied in the definition of new ret...

This work proposes a new Case-Based Reasoning (CBR) approach, named Q-CBR, that uses a Qualitative Spatial Reasoning (QSR) theory to model, retrieve and reuse cases by means of spatial relations. We used the EOPRA formalism to model the qualitative relations between the objects in a case and two algorithms were proposed: a new retrieval algorithm,...

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...

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,...

This paper proposes a new Case-Based Reasoning (CBR) approach, named Q-CBR, that uses a Qualitative Spatial Reasoning theory to model, retrieve and reuse cases by means of spatial relations. A qualitative distance and orientation calculus (\(\mathcal {EOPRA}\)) is used to model cases using qualitative relations between the objects in a case. A new...

This paper proposes a hybrid approach for solving the multi-objective model related to the minimisation of sugar cane waste collection costs and/or the maximisation of produced energy by this waste, with the aid of strategies for solving multi-objective problems, which transform the problem into a set of single-objective problems. This approach com...

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...

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...

Este trabalho busca o estudo do comportamento da Integração Numérica por Monte Carlo em um problema que tem como meta encontrar o volume e centro de massa de um objeto cuja função não é bem comportada ou definida. Realizando estes experimentos ficou comprovada a robustez do método que apresentou ótimos resultados, e que este método pode se estender...

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...

A radiotherapy planning is considered optimal when all the parameters involved, physical or biological, have been investigated and appropriate for each patient. In this type of planning, the major concern is with the tumor irradiation with the minimum possible damage to healthy tissues of the irradiated region, especially the organs at risk. The op...

It is well known that Brazil is the largest producer of sugarcane in the world. Nevertheless, a great concern exists about the crop system used, because the most common practice is manual harvesting with prior straw burning. The Brazilian authorities have approved a law prohibiting the burning of sugarcane crop residue before harvesting. However, m...

The aim of this work is the application of the Interior Point and Branch and Bound methods in multiobjective optimization models related to sugarcane harvest residual biomass. These methods showed their viability to help on choosing the sugarcane planting varieties, searching to optimize cost and energy balance of harvest residual biomass, which ha...

The project aims to realize the Primal-Dual Interior Point method and the Branch and Bound method implementation and the application on sugarcane trash utilization problems. The objective is to use the cited methods to determine the choice of cane varieties that provides the least possible amount of waste or generate the maximum amount of produced...