Place sequence learning for navigation

Abstract. A model of the hippocampus as a \cognitive map", inspired by the models of Burgess et al. (1994) and Jensen et al. (1996), is proposed. Simulations show that the resulting navigation behavior is as e-cient as the behavior exhibited by previous models. However, the architecture of the proposed model and the mechanisms governing the temporal characteristics of the neurons in the model are more realistic. In particular, the proposed model assigns distinct and speci c roles to the entorhinal cortex, the dentate gyrus and the hippocampal CA3 region. In essence, the dentate gyrus could act as a short-term memory that maintains the representation of a sequence of recently visited places. It could then force the corresponding CA3 place cells to re and to learn the spatial relationships between places through a Hebbian rule. This \topological representation " could then serve as a basis for predicting places ahead of the animal and drive \goal cells", i.e. cells that represent the direction to the goal, as proposed by Burgess et al.

Integration of navigation and action selection functionalities in a computational model of cortico-basal ganglia-thalamo-cortical loops

This article describes a biomimetic control architecture affording an animat both action selection and navigation functionalities. It satisfies the survival constraint of an artificial metabolism and supports several complementary navigation strategies. It builds upon an action selection model based on the basal ganglia of the vertebrate brain, using two interconnected cortico-basal ganglia-thalamo-cortical loops: a ventral one concerned with appetitive actions and a dorsal one dedicated to consummatory actions. The performances of the resulting model are evaluated in simulation. The experiments assess the prolonged survival permitted by the use of high level navigation strategies and the complementarity of navigation strategies in dynamic environments. The correctness of the behavioral choices in situations of antagonistic or synergetic internal states are also tested. Finally, the modelling choices are discussed with regard to their biomimetic plausibility, while the experimental results are estimated in terms of animat adaptivity

Spatial exploration, map learning, and self-positioning with MonaLysa

This paper describes how the MonaLysa control architecture implements a route-following navigation strategy. Two procedures that allow map building and self-positioning are described, and experimental results are provided that demonstrate that such procedures are robust with respect to noise. This approach is compared to others with similar objectives, and directions for future work are outlined.

The Animat Approach : Simulation of adaptive behavior in Animals and Robots

International audienc

Evolutionary Approaches to Neural Control in Mobile Robots

This article is centered on the application of evolutionary techniques to the automatic design of neural controllers for mobile robots. About 30 papers are reviewed and classified in a framework that takes into account the specific robots involved, the behaviors that are evolved, the characteristics of the corresponding neural controllers, how these controllers are genetically encoded, and whether or not an individual learning process complements evolution. Related research efforts in evolutionary robotics are occasionally cited. If it is yet unclear whether such approaches will scale up with increasing complexity, foreseeable bottlenecks and prospects of improvement are discussed in the text. Keywords--- Evolutionary Robotics, Neural Networks, Control Architectures, Behavior. I. Introduction T HE design of the control architecture of a robot able to fulfil its mission in changing and possibly unpredictable environments is a highly challenging task for a human. This is due to the v..

The Animat Approach : Simulation of adaptive behavior in Animals and Robots

International audienc

Evolutionary Approaches to Walking and Higher-Level Behaviors in 6-Legged Animats

This article describes the main current research project in evolutionary robotics at the AnimatLab, Paris. It aims at using an artificial selection process to automatically generate neural networks that control walking animats, i.e., simulated insects or real legged-robots. Essentially, it complements an underlying evolutionary process with a developmental procedure -- in order to reduce the size of the genotypic space that is explored -- and it calls upon an incremental approach -- in order to capitalize upon previously found solutions to simpler problems for solving problems of increasing difficulties. Thi

Le projet Psikharpax: objectifs et réalisations

National audienc

Evolution and development of control architectures in animats

This paper successively describes the works of Boers & Kuiper, Gruau, Cangelosi et al., Vaario, Dellaert & Beer, and Sims, which all evolve the developmental program of an arti�cial nervous system. The potentialities of these approaches for automatically devising a control architecture linking the perceptions and the actions of an animat are then discussed, together with their possible contributions to the fundamental issue of assessing the adaptive values of development, learning and evolution