References of "Antonelo, Eric Aislan 50025912"
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See detailModeling Multiple Autonomous Robot Behaviors and Behavior Switching with a Single Reservoir Computing Network
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Proceedings of the 2008 IEEE International Conference on Systems, Man and Cybernetics (2008)

Reservoir computing (RC) uses a randomly created Recurrent Neural Network as a reservoir of rich dynamics which projects the input to a high dimensional space. These projections are mapped to the desired ... [more ▼]

Reservoir computing (RC) uses a randomly created Recurrent Neural Network as a reservoir of rich dynamics which projects the input to a high dimensional space. These projections are mapped to the desired output using a linear output layer, which is the only part being trained by standard linear regression. In this work, RC is used for imitation learning of multiple behaviors which are generated by different controllers using an intelligent navigation system for mobile robots previously published in literature. Target seeking and exploration behaviors are conflicting behaviors which are modeled with a single RC network. The switching between the learned behaviors is implemented by an extra input which is able to change the dynamics of the reservoir, and in this way, change the behavior of the system. Experiments show the capabilities of Reservoir Computing for modeling multiple behaviors and behavior switching. [less ▲]

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See detailEvent detection and localization in mobile robot navigation using reservoir computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Dutoit, Xavier et al

in Artificial Neural Networks -- ICANN 2007 (2007)

Reservoir Computing (RC) uses a randomly created recur- rent neural network where only a linear readout layer is trained. In this work, RC is used for detecting complex events in autonomous robot navi ... [more ▼]

Reservoir Computing (RC) uses a randomly created recur- rent neural network where only a linear readout layer is trained. In this work, RC is used for detecting complex events in autonomous robot navi- gation. This can be extended to robot localization based solely on sensory information. The robot thus builds an implicit map of the environment without the use of odometry data. These techniques are demonstrated in simulation on several complex and even dynamic environments. [less ▲]

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See detailGenerative Modeling of Autonomous Robots and their Environments using Reservoir Computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Campenhout, Jan Van

in Neural Processing Letters (2007), 26(3), 233--249

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See detailExperiments with Reservoir Computing on the road sign problem
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Proceedings of the VIII Brazilian Congress on Neural Networks (CBRN) (2007)

The road sign problem is tackled in this work with Reservoir Computing (RC) networks. These networks are made of a fixed recurrent neural network where only a readout layer is trained. In the road sign ... [more ▼]

The road sign problem is tackled in this work with Reservoir Computing (RC) networks. These networks are made of a fixed recurrent neural network where only a readout layer is trained. In the road sign problem, an agent has to decide at some point in time which action to take given relevant information gathered in the past. We show that RC can handle simple and complex T-maze tasks (which are a subdomain of the road sign problem). [less ▲]

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See detailA Neural Reinforcement Learning Approach for Behavior Acquisition in Intelligent Autonomous Systems
Antonelo, Eric Aislan UL

Bachelor/master dissertation (2006)

In this work new artificial learning and innate control mechanisms are proposed for application in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots) existent in the ... [more ▼]

In this work new artificial learning and innate control mechanisms are proposed for application in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots) existent in the literature is enhanced with respect to its capacity of exploring the environment and avoiding risky configurations (that lead to collisions with obstacles even after learning). The particular autonomous system is based on modular hierarchical neural networks. Initially,the autonomous system does not have any knowledge suitable for exploring the environment (and capture targets œ foraging). After a period of learning,the system generates efficientobstacle avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky configurations) are discussed and the new learning and controltechniques (applied to the autonomous system) are verified through simulations. It is shown the effectiveness of the proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and decrease their probability of appearance in the future and the number of collisions in risky situations is significantly decreased. Experiments also consider maze environments (with targets distant from each other) and dynamic environments (with moving objects). [less ▲]

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See detailModular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors
Antonelo, Eric Aislan UL; Baerlvedt, Albert-Jan; Rognvaldsson, Thorsteinn et al

in The 2006 IEEE International Joint Conference on Neural Network Proceedings (2006)

Classical reinforcement learning mechanisms and a modular neural network are unified to conceive an intelligent autonomous system for mobile robot navigation. The conception aims at inhibiting two common ... [more ▼]

Classical reinforcement learning mechanisms and a modular neural network are unified to conceive an intelligent autonomous system for mobile robot navigation. The conception aims at inhibiting two common navigation deficiencies: generation of unsuitable cyclic trajectories and ineffectiveness in risky configurations. Different design apparatuses are considered to compose a system to tackle with these navigation difficulties, for instance: 1) neuron parameter to simultaneously memorize neuron activities and function as a learning factor, 2) reinforcement learning mechanisms to adjust neuron parameters (not only synapse weights), and 3) a inner-triggered reinforcement. Simulation results show that the proposed system circumvents difficulties caused by specific environment configurations, improving the relation between collisions and captures. [less ▲]

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See detailIntelligent autonomous navigation for mobile robots: spatial concept acquisition and object discrimination
Antonelo, Eric Aislan UL; Figueiredo, Mauricio; Baerlvedt, Albert-Jan et al

in Proceedings of the 6th IEEE International Symposium on Computational Intelligence in Robotics and Automation (2005)

An autonomous system able to construct its own navigation strategy for mobile robots is proposed. The navigation strategy is molded from navigation experiences (succeeding as the robot navigates ... [more ▼]

An autonomous system able to construct its own navigation strategy for mobile robots is proposed. The navigation strategy is molded from navigation experiences (succeeding as the robot navigates) according to a classical reinforcement learning procedure. The autonomous system is based on modular hierarchical neural networks. Initially the navigation performance is poor (many collisions occur). Computer simulations show that after a period of learning the autonomous system generates efficient obstacle avoidance and target seeking behaviors. Experiments also offer support for concluding that the autonomous system develops a variety of object discrimination capability and of spatial concepts. [less ▲]

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See detailEvolutionary fuzzy system for architecture control in a constructive neural network
Calvo, R.; Figueiredo, M.; Antonelo, Eric Aislan UL

in 2005 International Symposium on Computational Intelligence in Robotics and Automation (2005)

This work describes an evolutionary system to control the growth of a constructive neural network for autonomous navigation. A classifier system generates Takagi-Sugeno fuzzy rules and controls the ... [more ▼]

This work describes an evolutionary system to control the growth of a constructive neural network for autonomous navigation. A classifier system generates Takagi-Sugeno fuzzy rules and controls the architecture of a constructive neural network. The performance of the mobile robot guides the evolutionary learning mechanism. Experiments show the efficiency of the classifier fuzzy system for analyzing if it is worth inserting a new neuron into the architecture. [less ▲]

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