References of "Stroobandt, Dirk"
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See detailUnsupervised Learning in Reservoir Computing: Modeling Hippocampal Place Cells for Small Mobile Robots
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Alippi, Cesare; Polycarpou, Marios; Panayiotou, Christos (Eds.) et al Artificial Neural Networks -- ICANN 2009 (2009)

Biological systems (e.g., rats) have efficient and robust localization abilities provided by the so called, place cells, which are found in the hippocampus of rodents and primates (these cells encode ... [more ▼]

Biological systems (e.g., rats) have efficient and robust localization abilities provided by the so called, place cells, which are found in the hippocampus of rodents and primates (these cells encode locations of the animal's environment). This work seeks to model these place cells by employing three (biologically plausible) techniques: Reservoir Computing (RC), Slow Feature Analysis (SFA), and Independent Component Analysis (ICA). The proposed architecture is composed of three layers, where the bottom layer is a dynamic reservoir of recurrent nodes with fixed weights. The upper layers (SFA and ICA) provides a self-organized formation of place cells, learned in an unsupervised way. Experiments show that a simulated mobile robot with 17 noisy short-range distance sensors is able to self-localize in its environment with the proposed architecture, forming a spatial representation which is dependent on the robot direction. [less ▲]

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See detailImitation Learning of an Intelligent Navigation System for Mobile Robots using Reservoir Computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Proceedings of the 10th Brazilian Symposium on Neural Networks (SBRN) (2008)

The design of an autonomous navigation system for mobile robots can be a tough task. Noisy sensors, unstructured environments and unpredictability are among the problems which must be overcome. Reservoir ... [more ▼]

The design of an autonomous navigation system for mobile robots can be a tough task. Noisy sensors, unstructured environments and unpredictability are among the problems which must be overcome. Reservoir computing (RC) uses a randomly created recurrent neural network (the reservoir) which functions as a temporal kernel of rich dynamics that projects the input to a high dimensional space. This projection is mapped into the desired output (only this mapping must be learned with standard linear regression methods).In this work, RC is used for imitation learning of navigation behaviors generated by an intelligent navigation system in the literature. Obstacle avoidance, exploration and target seeking behaviors are reproduced with an increase in stability and robustness over the original controller. Experiments also show that the system generalizes the behaviors for new environments. [less ▲]

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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 detailMobile Robot Control in the Road Sign Problem using Reservoir Computing Networks
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Proceedings of the IEEE Int. Conf. on Robotics and Automation (ICRA) (2008)

In this work we tackle the road sign problem with reservoir computing (RC) networks. The T-maze task (a particular form of the road sign problem) consists of a robot in a T-shaped environment that must ... [more ▼]

In this work we tackle the road sign problem with reservoir computing (RC) networks. The T-maze task (a particular form of the road sign problem) consists of a robot in a T-shaped environment that must reach the correct goal (left or right arm of the T-maze) depending on a previously received input sign. It is a control task in which the delay period between the sign received and the required response (e.g., turn right or left) is a crucial factor. Delayed response tasks like this one form a temporal problem that can be handled very well by RC networks. Reservoir computing is a biologically plausible technique which overcomes the problems of previous algorithms such as backpropagation through time - which exhibits slow (or non-) convergence on training. RC is a new concept that includes a fast and efficient training algorithm. We show that this simple approach can solve the T-maze task efficiently. [less ▲]

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See detailEvent detection and localization for small mobile robots using reservoir computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Neural Networks (2008), 21(6), 862--871

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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 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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