5 papers · 1 filter
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…
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…
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…
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…
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,…