@article {Antonelo2014, title = {On Learning Navigation Behaviors for Small Mobile Robots With Reservoir Computing Architectures}, journal = {IEEE Transactions on Neural Networks and Learning Systems}, volume = {26}, number = {4}, year = {2015}, pages = {763-780}, abstract = {

This paper proposes a general reservoir computing (RC) learning framework that can be used to learn navigation behaviors for mobile robots in simple and complex unknown partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) be fixed while only a linear readout output layer is trained. The proposed RC framework builds upon the notion of navigation attractor or behavior that can be embedded in the high-dimensional space of the reservoir after learning. The learning of multiple behaviors is possible because the dynamic robot behavior, consisting of a sensory- motor sequence, can be linearly discriminated in the high- dimensional nonlinear space of the dynamic reservoir. Three learning approaches for navigation behaviors are shown in this paper. The first approach learns multiple behaviors based on the examples of navigation behaviors generated by a supervisor, while the second approach learns goal-directed navigation behaviors based only on rewards. The third approach learns complex goal- directed behaviors, in a supervised way, using a hierarchical architecture whose internal predictions of contextual switches guide the sequence of basic navigation behaviors toward the goal.

}, doi = {10.1109/TNNLS.2014.2323247}, author = {Eric A Antonelo and Benjamin Schrauwen} } @article {Antonelo2011, title = {Learning Slow Features with Reservoir Computing for Biologically-inspired Robot Localization}, journal = {Neural Networks}, volume = {25}, number = {1}, year = {2012}, pages = {178-190}, abstract = {

This work proposes a hierarchical biologically-inspired architecture for learning sensor-based spatial representations of a robot environment in an unsupervised way. The first layer is comprised of a fixed randomly generated recurrent neural network, the reservoir, which projects the input into a high-dimensional, dynamic space. The second layer learns instantaneous slowly-varying signals from the reservoir states using Slow Feature Analysis (SFA), whereas the third layer learns a sparse coding on the SFA layer using Independent Component Analysis (ICA). While the SFA layer generates non-localized activations in space, the ICA layer presents high place selectivity, forming a localized spatial activation, characteristic of place cells found in the hippocampus area of the rodent\’s brain. We show that, using a limited number of noisy short-range distance sensors as input, the proposed system learns a spatial representation of the environment which can be used to predict the actual location of simulated and real robots, without the use of odometry. The results confirm that the reservoir layer is essential for learning spatial representations from low-dimensional input such as distance sensors. The main reason is that the reservoir state reflects the recent history of the input stream. Thus, this fading memory is essential for detecting locations, mainly when locations are ambiguous and characterized by similar sensor readings.

}, author = {Eric A Antonelo and Benjamin Schrauwen} } @conference {Antonelo2011a, title = {Learning navigation attractors for mobile robots with reinforcement learning and reservoir computing}, booktitle = {Proceedings of the X Brazilian Congress on Computational Intelligence (CBIC)}, year = {2011}, month = {Nov.}, address = {Fortaleza, Brazil}, abstract = {Autonomous robot navigation in partially observable environments is a complex task because the state of the environment can not be completely determined only by the current sensory readings of a robot. This work uses the recently introduced paradigm for training recurrent neural networks (RNNs), called reservoir computing (RC), to model multiple navigation attractors in partially observable environments. In RC, the RNN with randomly generated fixed weights, called reservoir, projects the input into a high-dimensional dynamic space. Only the readout output layer is trained using standard linear regression techniques, and in this work, is used to approximate the state-action value function. By using a policy iteration framework, where an alternating sequence of policy improvement (samples generation from environment interaction) and policy evaluation (network training) steps are performed, the system is able to shape navigation attractors so that, after convergence, the robot follows the correct trajectory towards the goal. The experiments are accomplished using an e-puck robot extended with 8 distance sensors in a rectangular environment with an obstacle between the robot and the target region. The task is to reach the goal through the correct side of the environment, which is indicated by a temporary stimulus previously observed at the beginning of the episode. We show that the reservoir-based system (with short-term memory) can model these navigation attractors, whereas a feedforward network without memory fails to do so.}, author = {Eric A Antonelo and Stefan Depeweg and Benjamin Schrauwen} } @proceedings {Antonelo2010, title = {Supervised Learning of Internal Models for Autonomous Goal-oriented Robot Navigation using Reservoir Computing}, journal = {Proceedings of the IEEE International Conference on Robotics and Automation}, year = {2010}, month = {May}, pages = {2959-2964}, address = {Anchorage, AK}, abstract = {

In this work we propose a hierarchical architec- ture 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.

}, doi = {10.1109/ROBOT.2010.5509212}, author = {Eric A Antonelo and Benjamin Schrauwen} } @proceedings {Antonelo2009b, title = {On Different Learning Approaches with Echo State Networks for Localization of Small Mobile Robots}, journal = {Proceedings of the IX Brazilian Conference on Neural Networks (CBRN)}, year = {2009}, publisher = {SBRN}, abstract = {

Animals such as rats have innate and robust localization capabilities which allow them to navigate to goals in a maze. The rodent\’s hippocampus, with the so called place cells, is responsible for such spatial processing. This work seeks to model these place cells using either supervised or unsupervised learning techniques. More specifically, we use a randomly generated recurrent neural network (the reservoir) as a non-linear temporal kernel to expand the input to a rich dynamic space. The reservoir states are linearly combined (using linear regression) or, in the unsupervised case, are used for extracting slowly-varying features from the input to form place cells (the architectures are organized in hierarchical layers). Experiments show that a small mobile robot with cheap and low-range distance sensors can learn to self-localize in its environment with the proposed systems.

}, doi = {10.21528/CBRN2009-067}, url = {http://abricom.org.br/en/events/cbrn_2009/067_CBRN2009/}, author = {Eric A Antonelo and Benjamin Schrauwen} } @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 {Antonelo2009a, title = {Towards Autonomous Self-localization of Small Mobile Robots using Reservoir Computing and Slow Feature Analysis}, journal = {Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, year = {2009}, pages = {3818{\textendash}3823}, abstract = {

Biological systems such as rats have special brain structures which process spatial information from the envi- ronment. They have efficient and robust localization abilities provided by special neurons in the hippocampus, namely place cells. This work proposes a biologically plausible architecture which is based on three recently developed techniques: Reservoir Computing (RC), Slow Feature Analysis (SFA), and Independent Component Analysis (ICA). The bottom layer of our RC-SFA architecture is a reservoir of recurrent nodes which process the information from the robot\’s distance sensors. It provides a temporal kernel of rich dynamics which is used by the upper two layers (SFA and ICA) to autonomously learn place cells. Experiments with an e-puck robot with 8 infra-red sensors (which measure distances in [4-30] cm) show that the learning system based on RC-SFA provides a self-organized formation of place cells that can either distinguish between two rooms or to detect the corridor connecting them.

}, doi = {10.1109/ICSMC.2009.5346617}, author = {Eric A Antonelo and Benjamin Schrauwen} } @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} } @article {Antonelo2007b, title = {Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing}, journal = {Neural Processing Letters}, volume = {26}, number = {3}, year = {2007}, pages = {233{\textendash}249}, abstract = {

Autonomous mobile robots form an important research topic in the field of robotics due to their near-term applicability in the real world as domestic service robots. These robots must be designed in an efficient way using training sequences. They need to be aware of their position in the environment and also need to create models of it for deliberative planning. These tasks have to be performed using a limited number of sensors with low accuracy, as well as with a restricted amount of computational power. In this contribution we show that the recently emerged paradigm of Reservoir Computing (RC) is very well suited to solve all of the above mentioned problems, namely learning by example, robot localization, map and path generation. Reservoir Computing is a technique which enables a system to learn any time-invariant filter of the input by training a simple linear regressor that acts on the states of a high- dimensional but random dynamic system excited by the inputs. In addition, RC is a simple technique featuring ease of training, and low computational and memory demands.

}, doi = {http://dx.doi.org/10.1007/s11063-007-9054-9}, author = {Eric A Antonelo and Benjamin Schrauwen and Jan Van Campenhout} }