no code implementations • 19 Sep 2020 • Filipe Mutz, Thiago Oliveira-Santos, Avelino Forechi, Karin S. Komati, Claudine Badue, Felipe M. G. França, Alberto F. de Souza
In this work, we provide data for such analysis by comparing the accuracy of a particle filter localization when using occupancy, reflectivity, color, or semantic grid maps.
7 code implementations • 5 Dec 2019 • Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, Jürgen Schmidhuber
Many of its general principles are outlined in a companion report; the goal of this paper is to develop a practical learning algorithm and show that this conceptually simple perspective on agent training can produce a range of rewarding behaviors for multiple episodic environments.
no code implementations • 14 Jan 2019 • Claudine Badue, Rânik Guidolini, Raphael Vivacqua Carneiro, Pedro Azevedo, Vinicius Brito Cardoso, Avelino Forechi, Luan Ferreira Reis Jesus, Rodrigo Ferreira Berriel, Thiago Meireles Paixão, Filipe Mutz, Thiago Oliveira-Santos, Alberto Ferreira De Souza
In this survey, we present the typical architecture of the autonomy system of self-driving cars.
Robotics
no code implementations • 4 Oct 2018 • Thomas Teixeira, Filipe Mutz, Karin Satie Komati, Lucas Veronese, Vinicius B. Cardoso, Claudine Badue, Thiago Oliveira-Santos, Alberto F. de Souza
The objective of map decay is to correct invalid occupancy probabilities of map cells that are unobservable by sensors.
1 code implementation • ICLR 2019 • Paulo Rauber, Avinash Ummadisingu, Filipe Mutz, Juergen Schmidhuber
A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy.