4 papers · 1 filter
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…
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…