@proceedings {Waegeman2009, title = {Modular reservoir computing networks for imitation learning of multiple robot behaviors}, journal = {Proc. of the IEEE Int. Symp. on Computational Intelligence in Robotics and Automation (CIRA)}, year = {2009}, pages = {27-32}, abstract = {

Autonomous mobile robots must accomplish tasks in unknown and noisy environments. In this context, learning robot behaviors in an imitation based approach would be desirable in the perspective of service robotics as well as of learning robots. In this work, we use Reservoir Computing (RC) for learning robot behaviors by demonstration. In RC, a randomly generated recurrent neural network, the reservoir, projects the input to a dynamic temporal space. The reservoir states are mapped into a readout output layer which is the solely part being trained using standard linear regression. In this paper, we use a two layered modular structure, where the first layer comprises two RC networks, each one for learning primitive behaviors, namely, obstacle avoidance and target seeking. The second layer is composed of one RC network for behavior combination and coordination. The hierarchical RC network learns by examples given by simple controllers which implement the primitive behaviors. We use a simulation model of the e-puck robot which has distance sensors and a camera that serves as input for our system. The experiments show that, after training, the robot learns to coordinate the Goal Seeking (GS) and the Object Avoidance (OA) behaviors in unknown environments, being able to capture targets and navigate efficiently.

}, doi = {10.1109/CIRA.2009.5423194}, author = {Waegeman, Tim and Eric A Antonelo and wyffels, Francis and Benjamin Schrauwen} } @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} }