AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Extracting Situation Facts from Activation Value Histories in Behavior-Based Robots
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
2005 Special issue: Robust self-localisation and navigation based on hippocampal place cells
Neural Networks - Special issue: Computational theories of the functions of the hippocampus
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing
Neural Processing Letters
The introduction of time-scales in reservoir computing, applied to isolated digits recognition
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Event detection and localization in mobile robot navigation using reservoir computing
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
IEEE Transactions on Neural Networks
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Memory in linear recurrent neural networks in continuous time
Neural Networks
Modular reservoir computing networks for imitation learning of multiple robot behaviors
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
What makes a brain smart? reservoir computing as an approach for general intelligence
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Recurrent kernel machines: Computing with infinite echo state networks
Neural Computation
An approach to reservoir computing design and training
Expert Systems with Applications: An International Journal
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Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent 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 localization 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 input 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.