142 citations · 212 across the 20 of their papers we have counts for
13 papers · 1 filter
Disagreement-Regularized Imitation Learning for Image-Based Continuous Control with Gaussian and Beta Policies
Irving Giovani Bronzatti Petrazzini, Eric Aislan Antonelo
Purpose: Behavior cloning can accumulate errors when a learned controller visits states outside the demonstrated distribution. This study evaluates whether Disagreement-Regularized…
A Low-Cost, Open Platform for End-to-End Autonomous Driving on a Miniature Ackermann Vehicle
Gustavo Claudio Karl Couto, Eric Aislan Antonelo, Gabriel George Zipperer
This paper presents a low-cost, open experimental platform for research in end-to-end autonomous driving with miniature Ackermann vehicles. The platform combines a physical vehicle…
A Study of Conditional Diffusion Models for Open-Loop Control under Dry Friction and Stiction
Eric Aislan Antonelo
Diffusion models have recently emerged as expressive generative priors for planning and control. This paper studies Action Diffusion, an action-sequence diffusion formulation used…
Exploring multimodal implicit behavior learning for vehicle navigation in simulated cities
Eric Aislan Antonelo, Gustavo Claudio Karl Couto, Christian Möller
Standard Behavior Cloning (BC) fails to learn multimodal driving decisions, where multiple valid actions exist for the same scenario. We explore Implicit Behavioral Cloning (IBC) w…
Physics-Informed Neural Networks for Control of Single-Phase Flow Systems Governed by Partial Differential Equations
Luis Kin Miyatake, Eduardo Camponogara, Eric Aislan Antonelo +1
The modeling and control of single-phase flow systems governed by Partial Differential Equations (PDEs) present challenges, especially under transient conditions. In this work, we…
Physics-Informed Echo State Networks for Modeling Controllable Dynamical Systems
Eric Mochiutti, Eric Aislan Antonelo, Eduardo Camponogara
Echo State Networks (ESNs) are recurrent neural networks usually employed for modeling nonlinear dynamic systems with relatively ease of training. By incorporating physical laws in…
Physics-Informed Neural Networks with Skip Connections for Modeling and Control of Gas-Lifted Oil Wells
Jonas Ekeland Kittelsen, Eric Aislan Antonelo, Eduardo Camponogara +1
Neural networks, while powerful, often lack interpretability. Physics-Informed Neural Networks (PINNs) address this limitation by incorporating physics laws into the loss function,…
Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments
Gustavo Claudio Karl Couto, Eric Aislan Antonelo
Deriving robust control policies for realistic urban navigation scenarios is not a trivial task. In an end-to-end approach, these policies must map high-dimensional images from the…
Vertex-based reachability analysis for verifying ReLU deep neural networks
João Zago, Eduardo Camponogara, Eric Antonelo
Neural networks achieved high performance over different tasks, i.e. image identification, voice recognition and other applications. Despite their success, these models are still v…
Investigation of Proper Orthogonal Decomposition for Echo State Networks
Jean Panaioti Jordanou, Eric Aislan Antonelo, Eduardo Camponogara +1
Echo State Networks (ESN) are a type of Recurrent Neural Network that yields promising results in representing time series and nonlinear dynamic systems. Although they are equipped…
Proximal Policy Optimization with Continuous Bounded Action Space via the Beta Distribution
Irving G. B. Petrazzini, Eric A. Antonelo
Reinforcement learning methods for continuous control tasks have evolved in recent years generating a family of policy gradient methods that rely primarily on a Gaussian distributi…
Physics-Informed Neural Nets for Control of Dynamical Systems
Eric Aislan Antonelo, Eduardo Camponogara, Laio Oriel Seman +3
Physics-informed neural networks (PINNs) impose known physical laws into the learning of deep neural networks, making sure they respect the physics of the process while decreasing…
Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
Patrick Glauner, Angelo Migliosi, Jorge Meira +3
Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countri…