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20222025
most citedDouble Deep Reinforcement Learning Techniques for Low Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots

2 citations · 2 across the 8 of their papers we have counts for

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cs.RO2025

World Models for Autonomous Navigation of Terrestrial Robots from LIDAR Observations

Raul Steinmetz, Fabio Demo Rosa, Victor Augusto Kich +3

Autonomous navigation of terrestrial robots using Reinforcement Learning (RL) from LIDAR observations remains challenging due to the high dimensionality of sensor data and the samp…

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

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★ 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

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…