Research

In this work we propose a hierarchical architecture which constructs internal models of a robot environment for goal-oriented navigation by an imitation learning process. The proposed architecture is based on the Reservoir Computing paradigm for training Recurrent Neural Networks (RNN). It is composed of two randomly generated RNNs (called reservoirs), one for modeling the localization capability and one for learning the navigation skill. The localization module is trained to detect the current and previously visited robot rooms based only on 8 noisy infra-red distance sensors. These predictions together with distance sensors and the desired goal location are used by the navigation network to actually steer the robot through the environment in a goal-oriented manner. The training of this architecture is performed in a supervised way (with examples of trajectories created by a supervisor) using linear regression on the reservoir states. So, the reservoir acts as a temporal kernel projecting the inputs to a rich feature space, whose states are linearly combined to generate the desired outputs. Experimental results on a simulated robot show that the trained system can localize itself within both simple and large unknown environments and navigate successfully to desired goals.

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1. Eric Antonelo and Benjamin Schrauwen On Learning Navigation Behaviors for Small Mobile Robots with Reservoir Computing Architectures IEEE Transactions on Neural Networks and Learning Systems, Vol. 26 pp. 763-780 (2014). DOI: 10.1109/TNNLS.2014.2323247.  
2. Eric Antonelo and Benjamin Schrauwen Supervised learning of internal models for autonomous goal-oriented robot navigation using Reservoir Computing IEEE International conference on Robotics and Automation, Proceedings, pp. 6 (2010)   

With Prof. Thorsteinn Rögnvaldsson, from Halmstad University, Sweden, we are looking at how Reservoir Computing can help in deviation detection in a fleet of Swedish city buses using a signal from the air tank pressure from the buses in order to predict when a bus is going to break well in advance.

Video from the project at Halmstad University:

Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesirable phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of the downhole pressure measurement.

Given the importance of this measurement and the unreliability of the related sensor, this work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principled model simulation of a vertical riser model; and another for estimating the downhole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements. Both tasks are tackled by employing Echo State Networks (ESNs) as an efficient technique for training Recurrent Neural Networks.

We show that a single ESN is capable of robustly modeling both the slugging flow behavior and a steady state based only on a square wave input signal representing the production choke opening in the vertical riser. Besides, we compare the performance of a standard network to the performance of a multiple timescale hierarchical architecture in the second task and show that for some periods the latter architecture performs better. 

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)