Machine Learning

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.

Video showing trained RC network controlling the robot:

Publications

1. Eric Antonelo, Benjamin Schrauwen and Dirk Stroobandt Mobile Robot Control in the Road Sign Problem using Reservoir Computing Networks Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 911-916 (2008)    
2. Eric Antonelo, Benjamin Schrauwen and Jan Van Campenhout Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing Neural Processing Letters, Vol. 26(3), pp. 233-249 (2007)