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 high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able...

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 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...

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...

Within the context of learning sequences of basic tasks to build a complex behavior a method is proposed to coordinate a hierarchical set of tasks Each one pos sesses a set of sub tasks lower in the hierarchy which must be coordinated to respect a binary perceptive con straint For each task the coordination is achieved by a reinforcement learning inspired algorithm based on an heuristic which d...

A novel multi-cellular electronic circuit capable of evolution and development is described here. The circuit is composed of identical cells whose shape and location in the system is arbitrary. Cells all contain the complete genetic description of the final system, as in living organisms. Through a mechanism of development, cells connect to each other using a fully distributed hardware routing ...

Tim Chapman Department of Psychology, University of Stirling, Scotland, FK9 4DN, UK [email protected] Adam T. Hayes MS136-93, California Institute of Technology, Pasadena, CA 91125, USA [email protected] Mark W. Tilden MSA454, Los Alamos National Laboratory, Los Alamos, NM 87545, USA [email protected] Abstract We describe the design and testing of a novel biologically-inspired wi...

Membrane controllers have been developed using Numerical P Systems and their extension, Enzymatic Numerical P Systems, for controlling mobile robots like epuck and Khepera III. In this paper we prove that membrane controllers can be easily adapted for other types of robotic platforms. Therefore, obstacle avoidance and follower behaviors were adapted for Koala robots. The membrane controllers fo...