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20222024
most citedDouble Deep Reinforcement Learning Techniques for Low Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

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.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.RO20232 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…