@proceedings {Antonelo2009, title = {Unsupervised Learning in Reservoir Computing: Modeling Hippocampal Place Cells for Small Mobile Robots}, journal = {ICANN {\textquoteright}09: Proceedings of the 19th International Conference on Artificial Neural Networks}, volume = {5768}, year = {2009}, pages = {747-756}, publisher = {Springer-Verlag}, address = {Berlin, Heidelberg}, abstract = {

Biological systems (e.g., rats) have efficient and robust local- ization 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 Com- puting (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 ar- chitecture, forming a spatial representation which is dependent on the robot direction.

}, issn = {978-3-642-04274-4}, doi = {http://dx.doi.org/10.1007/978-3-642-04274-4_77}, author = {Eric A Antonelo and Benjamin Schrauwen and Dirk Stroobandt} } @article {Antonelo2008b, title = {Event detection and localization for small mobile robots using reservoir computing}, journal = {Neural Networks}, volume = {21}, number = {6}, year = {2008}, pages = {862{\textendash}871}, abstract = {Reservoir Computing (RC) techniques use a fixed (usually randomly created) re- current neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localiza- tion tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments.}, doi = {doi:10.1016/j.neunet.2008.06.010}, author = {Eric A Antonelo and Benjamin Schrauwen and Dirk Stroobandt} } @proceedings {Antonelo2008a, title = {Imitation Learning of an Intelligent Navigation System for Mobile Robots using Reservoir Computing}, journal = {Proceedings of the 10th Brazilian Symposium on Neural Networks (SBRN)}, year = {2008}, pages = {93-98}, abstract = {

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.

}, issn = {978-0-7695-3361-2}, doi = {10.1109/SBRN.2008.32}, url = {https://ieeexplore.ieee.org/document/4665898/}, author = {Eric A Antonelo and Benjamin Schrauwen and Dirk Stroobandt} } @proceedings {Antonelo2008, title = {Mobile Robot Control in the Road Sign Problem using Reservoir Computing Networks}, journal = {Proceedings of the IEEE Int. Conf. on Robotics and Automation (ICRA)}, year = {2008}, pages = {911-916}, abstract = {

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.

}, issn = {978-1-4244-1646-2}, doi = {10.1109/ROBOT.2008.4543321}, url = {https://ieeexplore.ieee.org/document/4543321/}, author = {Eric A Antonelo and Benjamin Schrauwen and Dirk Stroobandt} } @conference {Antonelo2008c, title = {Modeling Multiple Autonomous Robot Behaviors and Behavior Switching with a Single Reservoir Computing Network}, booktitle = {Proceedings of the IEEE International Conference on Systems, Man and Cybernetics}, year = {2008}, month = {Oct.}, pages = {1843{\textendash}1848}, publisher = {IEEE}, organization = {IEEE}, address = {Singapore}, abstract = {

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 imple- mented 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.

}, isbn = {978-1-4244-2383-5}, doi = {10.1109/ICSMC.2008.4811557}, url = {https://ieeexplore.ieee.org/document/4811557/}, author = {Eric A Antonelo and Benjamin Schrauwen and Dirk Stroobandt} } @proceedings {Antonelo2007, title = {Event detection and localization in mobile robot navigation using reservoir computing}, journal = {Artificial Neural Networks -- ICANN 2007}, volume = {Lecture Notes in Computer Science, vol 4669}, year = {2007}, pages = {660-669}, publisher = {Springer-Verlag}, edition = {de S{\'a} J.M., Alexandre L.A., Duch W., Mandic D. }, abstract = {

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.

}, keywords = {ESN}, issn = {978-3-540-74695-9}, doi = {10.1007/978-3-540-74695-9_68}, url = {https://link.springer.com/chapter/10.1007/978-3-540-74695-9_68}, author = {Eric A Antonelo and Benjamin Schrauwen and Xavier Dutoit and Dirk Stroobandt and Marnix Nuttin} } @proceedings {Antonelo2007a, title = {Experiments with Reservoir Computing on the road sign problem}, journal = {Proceedings of the VIII Brazilian Congress on Neural Networks (CBRN)}, year = {2007}, publisher = {SBRN}, abstract = {

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).

}, doi = {10.21528/CBRN2007-047}, url = {http://abricom.org.br/eventos/cbrn_2007/50100047-2/}, author = {Eric A Antonelo and Benjamin Schrauwen and Dirk Stroobandt} }