2 citations · 3 across the 22 of their papers we have counts for
24 papers
A Heterogeneous UAV UGV Methodology for Aerial Tracking of Mini Autonomous Cars
Santiago Fernandez, Angel da Silva, Hiago Sodre +3
Aerial tracking of autonomous ground vehicles can provide useful situational awareness in environments where fixed external perception infrastructure is unavailable. However, visua…
Stress-Query and Temporal Reliability of Vision-Language Models as copilots in Autonomous UAV Navigation
Hiago Sodre, Sebastian Barcelona, Vincent Sandin +3
Integrating Vision-Language Models (VLMs) into autonomous UAV navigation enables high-level semantic reasoning but introduces critical temporal bottlenecks that can destabilize fli…
Self Supervised Learning from Automatically Generated Demonstrations for Visual Robotic Manipulation
Andres Rivas, Anselmo R. Cukla, Rodrigo S. Guerra +2
Robotic manipulation often requires object specific programming, manual data annotation, or calibrated perception pipelines, which limits rapid deployment in practical settings. Le…
Reducing Latency in LLM-Based Natural Language Commands Processing for Robot Navigation
Diego Pollini, Bruna V. Guterres, Rodrigo S. Guerra +1
The integration of Large Language Models (LLMs), such as GPT, in industrial robotics enhances operational efficiency and human-robot collaboration. However, the computational compl…
Real-time Robotics Situation Awareness for Accident Prevention in Industry
Juan M. Deniz, Andre S. Kelboucas, Ricardo Bedin Grando
This study explores human-robot interaction (HRI) based on a mobile robot and YOLO to increase real-time situation awareness and prevent accidents in the workplace. Using object se…
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…
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…
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…
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…
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…
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…
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning
Matheus G. Mateus, Ricardo B. Grando, Paulo L. J. Drews-Jr
Unmanned Aerial Vehicles (UAV) have been standing out due to the wide range of applications in which they can be used autonomously. However, they need intelligent systems capable o…
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…
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…
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…
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…
Trajectory Planning for Hybrid Unmanned Aerial Underwater Vehicles with Smooth Media Transition
Pedro Miranda Pinheiro, Armando Alves Neto, Ricardo Bedin Grando +5
In the last decade, a great effort has been employed in the study of Hybrid Unmanned Aerial Underwater Vehicles, robots that can easily fly and dive into the water with different l…
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,…
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…