Francisco Leiva

Francisco Leiva
University of Chile · Departamento de Ingeniería Eléctrica

Master of Science

About

11
Publications
1,291
Reads
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106
Citations
Citations since 2016
11 Research Items
106 Citations
201620172018201920202021202205101520253035
201620172018201920202021202205101520253035
201620172018201920202021202205101520253035
201620172018201920202021202205101520253035
Introduction

Publications

Publications (11)
Article
In this paper, we propose a map-less visual navigation system for biped humanoid robots, which extracts information from color images to derive motion commands using Deep Reinforcement Learning (DRL). The map-less visual navigation policy is trained using the DDPG algorithm, which corresponds to an actor-critic DRL algorithm. The algorithm is imple...
Chapter
In this paper, we propose an end-to-end approach to endow indoor service robots with the ability to avoid collisions using Deep Reinforcement Learning (DRL). The proposed method allows a controller to derive continuous velocity commands for an omnidirectional mobile robot using depth images, laser measurements, and odometry based speed estimations....
Article
In this letter, we propose a robust approach to train map-less navigation policies that rely on variable size 2D point clouds, using Deep Reinforcement Learning (Deep RL). The navigation policies are trained in simulations using the DDPG algorithm. Through experimental evaluations in simulated and real-world environments, we showcase the benefits o...
Article
Full-text available
The goal of this paper is to describe a vision system for humanoid robot soccer players that does not use any color information, and whose object detectors are based on the use of convolutional neural networks. The main features of this system are the following: (i) real-time operation in computationally constrained humanoid robots, and (ii) the ab...
Preprint
This work proposes a scheme that allows learning complex multi-agent behaviors in a sample efficient manner, applied to 2v2 soccer. The problem is formulated as a Markov game, and solved using deep reinforcement learning. We propose a basic multi-agent extension of TD3 for learning the policy of each player, in a decentralized manner. To ease learn...
Article
Full-text available
This paper describes a system for the automatic determination of rock-breaking target poses for impact hammers used in underground mines. The rock-breaking target pose is defined as the position and angle at which the impact hammer must strike a rock in order to break it. The automatic determination of this pose is essential for the autonomous oper...
Chapter
The goal of this paper is to propose a vision system for humanoid robotic soccer that does not use any color information. The main features of this system are: (i) real-time operation in the NAO robot, and (ii) the ability to detect the ball, the robots, their orientations, the lines and key field features robustly. Our ball detector, robot detecto...
Conference Paper
Full-text available
In this paper, we propose an end-to-end approach to endow indoor service robots with the ability to avoid collisions using Deep Reinforcement Learning (DRL). The proposed method allows a controller to derive continuous velocity commands for an omnidirectional mobile robot using depth images, laser measurements, and odometry based speed estimations....
Preprint
Full-text available
The goal of this paper is to propose a vision system for hu-manoid robotic soccer that does not use any color information. The main features of this system are: (i) real-time operation in the NAO robot, and (ii) the ability to detect the ball, the robots, their orientations, the lines and key field features robustly. Our ball detector, robot detect...
Preprint
Full-text available
The goal of this paper is to propose a vision system for humanoid robotic soccer that does not use any color information. The main features of this system are: (i) real-time operation in the NAO robot, and (ii) the ability to detect the ball, the robots, their orientations, the lines and key field features robustly. Our ball detector, robot detecto...

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Cited By

Projects

Project (1)
Project
To develop new theories and application of DRL (Deep Reinforcement Learning) in Robotics.