This study describes how complex goal-directed behavior can be obtained through adaptation processes in a hierarchically organized recurrent neural network using a genetic algorithm (GA). Our experiments, using a simulated Khepera robot, showed that different types of dynamic structures self-organize in the lower and higher levels of the network for the purpose of achieving complex navigation t...

This study describes how complex goal-directed behavior can evolve in a hierarchically organized recurrent neural network controlling a simulated Khepera robot. Different types of dynamic structures self-organize in the lower and higher levels of a network for the purpose of achieving complex navigation tasks. The parametric bifurcation structures that appear in the lower level explain the mech...

In the Neuro-robotics Lab of the Centre for Hybrid Intelligent Systems we have organised various challenging undergraduate student projects using Mindstorm, Khepera and PeopleBot robots. In this paper we will describe a particularly interesting undergraduate student project involving the introduction of intelligent behaviour onto a mini-robot platform. In the past robots were mainly hardcoded t...

In this paper a new coevolutive method, called Uniform Coevolution, is introduced, to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn highperformance reactive behavior for navigation and collisions avoidance. The coevolutive method allows evolving the environment, to learn a general behavior able to solve the problem in different envi...

In this paper, we aim to design decision-making mechanisms for a simulated Khepera robot equipped with simple sensors, which integrates over time its perceptual experience in order to initiate a simple signalling response. Contrary to other previous similar studies, in this work the decision-making is uniquely controlled by the time-dependent structures of the agent controller, which in turn, a...

Behavioral robustness at antibody and immune network levels is discussed. The robustness of the immune response that drives an autonomous mobile robot is examined with computational experiments in the trajectory generation context in unknown environments. The immune response is met based on the immune network metaphor for different low-level behaviors coordination. These behaviors are activated...

An evolutionary algorithm for the creation of recurrent network structures is presented. The aim is to develop neural networks controlling the behaviour of miniature robots. Two neuro-modules are created separately using this evolutionary approach. The rst neuro-module gives the agents the ability to move within an environment without colliding with obstacles. The second neuro-module provides t...

This paper addresses the problem of autonomous behaviors of virtual characters. We postulate that a behavior is regarded as autonomous when the actions performed by the agent result from the interaction between its internal dynamics and the environment, rather than being externally controlled. In this work, we argue that an autonomous behavior is an agent’s solution to a given problem, which is...

This paper presents the intelligent wheeled mobile robot motion control in unstructured environments. The fuzzy control of a wheeled mobile robot motion in unstructured environments with obstacles and slopes is proposed. Outputs of the fuzzy controller are the angular speed difference between the left and right wheels of the mobile robot and the mobile robot velocity. The simulation results sho...

Learning affordances can be defined as learning action potentials, i.e., learning that an object exhibiting certain regularities offers the possibility of performing a particular action. We propose a method to endow an agent with the capability of acquiring this knowledge by relating the object invariants with the potentiality of performing an action via interaction episodes with each object. W...