
Francisco LeivaUniversity of Chile · Departamento de Ingeniería Eléctrica
Francisco Leiva
Master of Science
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16
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Introduction
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Publications
Publications (16)
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...
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...
This paper proposes a method to combine reinforcement learning (RL) and imitation learning (IL) using a dynamic, performance-based modulation over learning signals. The proposed method combines RL and behavioral cloning (IL), or corrective feedback in the action space (interactive IL/IIL), by dynamically weighting the losses to be optimized, taking...
This work presents a deep reinforcement learning-based approach to train controllers for the autonomous loading of ore piles with a Load-Haul-Dump (LHD) machine. These controllers must perform a complete loading maneuver, filling the LHD's bucket with material while avoiding wheel drift, dumping material, or getting stuck in the pile. The training...
This work proposes a scheme for learning how to break rocks with an impact hammer. The problem is formulated as a Partially Observable Markov's Decision Process, and then solved through deep reinforcement learning. We propose a simple formulation, requiring only a basic sensorization of the hammer's manipulator, and involving just two discrete acti...
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...
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...
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...
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....
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...
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....
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...
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...