Imitation Learning of an Intelligent Navigation System for Mobile Robots using Reservoir Computing

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 ▲]

Event detection and localization in mobile robot navigation using reservoir computing

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 ▲]

A Neural Reinforcement Learning Approach for Behavior Acquisition in Intelligent Autonomous Systems

Bachelor/master dissertation (2006)

In this work new artificial learning and innate control mechanisms are proposed for application in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots) existent in the ... [more ▼]

In this work new artificial learning and innate control mechanisms are proposed for application in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots) existent in the literature is enhanced with respect to its capacity of exploring the environment and avoiding risky configurations (that lead to collisions with obstacles even after learning). The particular autonomous system is based on modular hierarchical neural networks. Initially,the autonomous system does not have any knowledge suitable for exploring the environment (and capture targets œ foraging). After a period of learning,the system generates efficientobstacle avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky configurations) are discussed and the new learning and controltechniques (applied to the autonomous system) are verified through simulations. It is shown the effectiveness of the proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and decrease their probability of appearance in the future and the number of collisions in risky situations is significantly decreased. Experiments also consider maze environments (with targets distant from each other) and dynamic environments (with moving objects). [less ▲]

Evolutionary fuzzy system for architecture control in a constructive neural network

in 2005 International Symposium on Computational Intelligence in Robotics and Automation (2005)

This work describes an evolutionary system to control the growth of a constructive neural network for autonomous navigation. A classifier system generates Takagi-Sugeno fuzzy rules and controls the ... [more ▼]

This work describes an evolutionary system to control the growth of a constructive neural network for autonomous navigation. A classifier system generates Takagi-Sugeno fuzzy rules and controls the architecture of a constructive neural network. The performance of the mobile robot guides the evolutionary learning mechanism. Experiments show the efficiency of the classifier fuzzy system for analyzing if it is worth inserting a new neuron into the architecture. [less ▲]