References of "Schrauwen, Benjamin"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailLearning Slow Features with Reservoir Computing for Biologically-inspired Robot Localization
Antonelo, Eric Aislan UL; Schrauwen, Benjamin

in Neural Networks (2012), 25(1), 178-190

Detailed reference viewed: 31 (2 UL)
Full Text
Peer Reviewed
See detailLearning navigation attractors for mobile robots with reinforcement learning and reservoir computing
Antonelo, Eric Aislan UL; Depeweg, Stefan; Schrauwen, Benjamin

in Proceedings of the X Brazilian Congress on Computational Intelligence (CBIC) (2011)

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 16 (0 UL)
Full Text
Peer Reviewed
See detailSupervised Learning of Internal Models for Autonomous Goal-oriented Robot Navigation using Reservoir Computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin

in 2010 IEEE International Conference on Robotics and Automation (2010)

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 18 (1 UL)
Full Text
Peer Reviewed
See detailUnsupervised Learning in Reservoir Computing: Modeling Hippocampal Place Cells for Small Mobile Robots
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Alippi, Cesare; Polycarpou, Marios; Panayiotou, Christos (Eds.) et al Artificial Neural Networks -- ICANN 2009 (2009)

Biological systems (e.g., rats) have efficient and robust localization abilities provided by the so called, place cells, which are found in the hippocampus of rodents and primates (these cells encode ... [more ▼]

Biological systems (e.g., rats) have efficient and robust localization 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 Computing (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 architecture, forming a spatial representation which is dependent on the robot direction. [less ▲]

Detailed reference viewed: 14 (0 UL)
Full Text
Peer Reviewed
See detailOn Different Learning Approaches with Echo State Networks for Localization of Small Mobile Robots
Antonelo, Eric Aislan UL; Schrauwen, Benjamin

in Proceedings of the IX Brazilian Conference on Neural Networks (2009)

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 18 (0 UL)
Full Text
Peer Reviewed
See detailModular reservoir computing networks for imitation learning of multiple robot behaviors
Waegeman, Tim; Antonelo, Eric Aislan UL; wyffels, Francis et al

in Proc. of the 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA) (2009)

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 17 (2 UL)
Full Text
Peer Reviewed
See detailTowards Autonomous Self-localization of Small Mobile Robots using Reservoir Computing and Slow Feature Analysis
Antonelo, Eric Aislan UL; Schrauwen, Benjamin

in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2009)

Biological systems such as rats have special brain structures which process spatial information from the environment. They have efficient and robust localization abilities provided by special neurons in ... [more ▼]

Biological systems such as rats have special brain structures which process spatial information from the environment. 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. [less ▲]

Detailed reference viewed: 14 (0 UL)
Full Text
Peer Reviewed
See detailModeling Multiple Autonomous Robot Behaviors and Behavior Switching with a Single Reservoir Computing Network
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Proceedings of the 2008 IEEE International Conference on Systems, Man and Cybernetics (2008)

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 ... [more ▼]

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 implemented 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. [less ▲]

Detailed reference viewed: 17 (0 UL)
Full Text
Peer Reviewed
See detailImitation Learning of an Intelligent Navigation System for Mobile Robots using Reservoir Computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Proceedings of the 10th Brazilian Symposium on Neural Networks (SBRN) (2008)

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 15 (0 UL)
Full Text
Peer Reviewed
See detailMobile Robot Control in the Road Sign Problem using Reservoir Computing Networks
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Proceedings of the IEEE Int. Conf. on Robotics and Automation (ICRA) (2008)

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 13 (0 UL)
Full Text
Peer Reviewed
See detailEvent detection and localization for small mobile robots using reservoir computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Neural Networks (2008), 21(6), 862--871

Detailed reference viewed: 35 (0 UL)
Full Text
Peer Reviewed
See detailGenerative Modeling of Autonomous Robots and their Environments using Reservoir Computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Campenhout, Jan Van

in Neural Processing Letters (2007), 26(3), 233--249

Detailed reference viewed: 42 (2 UL)
Full Text
Peer Reviewed
See detailEvent detection and localization in mobile robot navigation using reservoir computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Dutoit, Xavier et al

in Artificial Neural Networks -- ICANN 2007 (2007)

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 13 (0 UL)
Full Text
Peer Reviewed
See detailExperiments with Reservoir Computing on the road sign problem
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Proceedings of the VIII Brazilian Congress on Neural Networks (CBRN) (2007)

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 ... [more ▼]

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). [less ▲]

Detailed reference viewed: 15 (0 UL)