activity
20212026
most citedDouble Deep Reinforcement Learning Techniques for Low Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots

2 citations · 4 across the 22 of their papers we have counts for

collaborators

22 papers

cs.RO2026

Cross domain Persistent Monitoring for Hybrid Aerial Underwater Vehicles

Ricardo B. Grando, Victor A. Kich, Alisson H. Kolling +3

Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs) have emerged as platforms capable of operating in both aerial and underwater environments, enabling applications such as inspect…

cs.RO2024

Parallel Distributional Deep Reinforcement Learning for Mapless Navigation of Terrestrial Mobile Robots

Victor Augusto Kich, Alisson Henrique Kolling, Junior Costa de Jesus +8

This paper introduces novel deep reinforcement learning (Deep-RL) techniques using parallel distributional actor-critic networks for navigating terrestrial mobile robots. Our appro…

cs.LG2024

CURLing the Dream: Contrastive Representations for World Modeling in Reinforcement Learning

Victor Augusto Kich, Jair Augusto Bottega, Raul Steinmetz +3

In this work, we present Curled-Dreamer, a novel reinforcement learning algorithm that integrates contrastive learning into the DreamerV3 framework to enhance performance in visual…

cs.LG2024

Kolmogorov-Arnold Network for Online Reinforcement Learning

Victor Augusto Kich, Jair Augusto Bottega, Raul Steinmetz +3

Kolmogorov-Arnold Networks (KANs) have shown potential as an alternative to Multi-Layer Perceptrons (MLPs) in neural networks, providing universal function approximation with fewer…

cs.CV2024

From Seedling to Harvest: The GrowingSoy Dataset for Weed Detection in Soy Crops via Instance Segmentation

Raul Steinmetz, Victor A. Kich, Henrique Krever +5

Deep learning, particularly Convolutional Neural Networks (CNNs), has gained significant attention for its effectiveness in computer vision, especially in agricultural tasks. Recen…

cs.RO2024

Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learning

Ricardo B. Grando, Raul Steinmetz, Victor A. Kich +7

Deep Reinforcement Learning (DRL) has emerged as a promising approach to enhancing motion control and decision-making through a wide range of robotic applications. While prior rese…

cs.RO2024★ 1 cited

Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments

Victor A. Kich, Muhammad A. Muttaqien, Junya Toyama +4

Recent advancements in real-time object detection frameworks have spurred extensive research into their application in robotic systems. This study provides a comparative analysis o…

cs.RO2024

Advancing Behavior Generation in Mobile Robotics through High-Fidelity Procedural Simulations

Victor A. Kich, Jair A. Bottega, Raul Steinmetz +3

This paper introduces YamaS, a simulator integrating Unity3D Engine with Robotic Operating System for robot navigation research and aims to facilitate the development of both Deep…

cs.RO2023

Enhanced Low-Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots Using Double Deep Reinforcement Learning Techniques

Linda Dotto de Moraes, Victor Augusto Kich, Alisson Henrique Kolling +4

In this study, we present two distinct approaches within the realm of Deep Reinforcement Learning (Deep-RL) aimed at enhancing mapless navigation for a ground-based mobile robot. T…

cs.RO2023

Parallel Distributional Prioritized Deep Reinforcement Learning for Unmanned Aerial Vehicles

Alisson Henrique Kolling, Victor Augusto Kich, Junior Costa de Jesus +4

This work presents a study on parallel and distributional deep reinforcement learning applied to the mapless navigation of UAVs. For this, we developed an approach based on the Sof…

cs.RO2023★ 1 cited

DoCRL: Double Critic Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition

Ricardo B. Grando, Junior C. de Jesus, Victor A. Kich +3

Deep Reinforcement Learning (Deep-RL) techniques for motion control have been continuously used to deal with decision-making problems for a wide variety of robots. Previous works s…

cs.RO2023★ 2 cited

Double Deep Reinforcement Learning Techniques for Low Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots

Linda Dotto de Moraes, Victor Augusto Kich, Alisson Henrique Kolling +6

In this work, we present two Deep Reinforcement Learning (Deep-RL) approaches to enhance the problem of mapless navigation for a terrestrial mobile robot. Our methodology focus on…

cs.RO2022

Jubileo: An Open-Source Robot and Framework for Research in Human-Robot Social Interaction

Jair A. Bottega, Victor A. Kich, Alisson H. Kolling +4

Human-robot interaction (HRI) is essential to the widespread use of robots in daily life. Robots will eventually be able to carry out a variety of duties in human civilization thro…

cs.RO2022

Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization

Ricardo B. Grando, Junior C. de Jesus, Victor A. Kich +3

Previous works showed that Deep-RL can be applied to perform mapless navigation, including the medium transition of Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs). This paper…

cs.RO2022

Deterministic and Stochastic Analysis of Deep Reinforcement Learning for Low Dimensional Sensing-based Navigation of Mobile Robots

Ricardo B. Grando, Junior C. de Jesus, Victor A. Kich +3

Deterministic and Stochastic techniques in Deep Reinforcement Learning (Deep-RL) have become a promising solution to improve motion control and the decision-making tasks for a wide…

cs.RO2022

Virtual Reality Platform to Develop and Test Applications on Human-Robot Social Interaction

Jair A. Bottega, Raul Steinmetz, Alisson H. Kolling +4

Robotics simulation has been an integral part of research and development in the robotics area. The simulation eliminates the possibility of harm to sensors, motors, and the physic…

cs.RO2022

Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles

Junior Costa de Jesus, Victor Augusto Kich, Alisson Henrique Kolling +3

Reinforcement Learning (RL) has presented an impressive performance in video games through raw pixel imaging and continuous control tasks. However, RL performs poorly with high-dim…

physics.app-ph2022

Practical Analysis of Permeable Concrete Properties with Polypropylene Fiber Addition

Rebeca de M. Kich, Victor A. Kich, Kelvin I. Seibt

One of the recent approaches used to minimize the impacts of the growth of impermeable areas in urban centers is permeable flooring. Permeable floors can be made of concrete and ar…

cs.NI2022

Bibliographic Analysis of the Capacity and Applicability of Li-Fi Networks

Kelvin I. Seibt, Victor A. Kich, Gabriel V. Heisler

This article aims to bring a metric of capacities and applicability of Light Fidelity (Li-Fi) as a communication system through a direct comparison to subareas of Wireless Fidelity…

cs.CV2022

Deep Reinforcement Learning Using a Low-Dimensional Observation Filter for Visual Complex Video Game Playing

Victor Augusto Kich, Junior Costa de Jesus, Ricardo Bedin Grando +3

Deep Reinforcement Learning (DRL) has produced great achievements since it was proposed, including the possibility of processing raw vision input data. However, training an agent t…

cs.RO2021

Double Critic Deep Reinforcement Learning for Mapless 3D Navigation of Unmanned Aerial Vehicles

Ricardo Bedin Grando, Junior Costa de Jesus, Victor Augusto Kich +2

This paper presents a novel deep reinforcement learning-based system for 3D mapless navigation for Unmanned Aerial Vehicles (UAVs). Instead of using a image-based sensing approach,…

cs.RO2021

Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition

Ricardo Bedin Grando, Junior Costa de Jesus, Victor Augusto Kich +5

Since the application of Deep Q-Learning to the continuous action domain in Atari-like games, Deep Reinforcement Learning (Deep-RL) techniques for motion control have been qualitat…