8 papers
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…
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…
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…
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…
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…
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…