The goal of this work is to augment reinforcement learning techniques for autonomous robot navigation with a state space encoding more representative of the actual state of the robot in its environment, than available from direct sensor readings. A second goal is to demonstrate the approach in a real-world setting, using the microrobot Khepera (K-Team, Lausanne, Switzerland). The choice of stat...

In this paper we investigate real-time adaptive extensions of our fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. The main idea is to introduce active battery level sensors and recharge zones to improve robot behavior for reaching survivability in environment exploration. In order to achieve this goal, we propose an impro...

This paper presents a predictive model of sensor readings for mobile robot. The model predicts sensor readings for given time horizon based on current sensor readings and velocities of wheels assumed for this horizon. Similar models for such anticipation have been proposed in the literature. The novelty of the model presented in the paper comes from the fact that its structure takes into accoun...

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...

The utilization of a multi-objective approach for evolving artificial neural networks that act as the controllers for phototaxis and radio frequency (RF) localization behaviors of a virtual Khepera robot simulated in a 3D, physics-based environment is discussed in this chapter. It explains the comparison performances among the elitism without archive and elitism with archive used in the evoluti...

This paper describes the saliency-based scene memory model of a mobile robot in which objects in salient spots are quickly learned and recognized based on the competitively growing neural network using temporal coding. This neural network represents objects using latency-based temporal coding and grows size and recognizability through self-organized learning with growth. In this model, objects ...

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...

In this paper we demonstrate an eecient method which divides a control task into smaller sub{tasks. We use a Genetic Programming system that rst learns the sub-tasks and then evolves a higher{level action selection strategy for deciding which of the evolved lower{level algorithms should be in control. The Swiss miniature robot Khepera is employed as the experimental platform. Results are presen...

We present a novel evolutionary approach to robotic control of a real robot based on genetic programming (GP). Our approach uses genetic programming techniques that manipulate machine code to evolve control programs for robots. This variant of GP has several advantages over a conventional GP system, such as higher speed, lower memory requirements and better real time properties. Previous attemp...