book on biomimetic neural learning for intelligent robots

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We are pleased to announce the new book

“Biomimetic Neural Learning for Intelligent Robots”

Stefan Wermter, Günther Palm, Mark Elshaw (Eds) 2005, Springer

This book presents research performed as part of the EU project on
biomimetic multimodal learning in a mirror neuron-based robot
(MirrorBot) and contributions presented at the International AI-Workshop
in NeuroBotics. The overall aim of the book is to present a broad
spectrum of current research into biomimetic neural learning for
intelligent autonomous robots. There seems to be a need for a new type
of robots which is inspired by nature and so performs in a more flexible
learned manner than current robots. This new type of robots is driven by
recent new theories and experiments in neuroscience indicating that a
biological and neuroscience-oriented approach could lead to new
life-like robotic systems.

The book focuses on some of the research progress made in the MirrorBot
project which uses concepts from mirror neurons as a basis for the
integration of vision, language and action. In this book we show the
development of new techniques using cell assemblies, associative neural
networks, and Hebbian-type learning in order to associate vision,
language and motor concepts. We have developed biomimetic multimodal
learning and language instruction in a robot to investigate the task of
searching for objects. As well as the research performed in this area
for the MirrorBot project, the second part of this book incorporates
significant contributes from essential research in the field of
biomimetic robotics. This second part of the book concentrates on the
progress made in neuroscience-inspired robotic learning approaches (in
short: Neuro-Botics).

We hope that this book stimulates and encourages new research in this area.

Further details can be found at

http://www.his.sunderland.ac.uk/mirrorbot/mirrorbook.html and

http://www.springeronline.com/sgw/cda/frontpage/0,11855,3-40109-22-55007983-0,00.html

Chapters

Towards Biomimetic Neural Learning for Intelligent Robots
Stefan Wermter, Günther Palm, Cornelius Weber and Mark Elshaw

The Intentional Attunement Hypothesis. The Mirror Neuron System and its
Role in Interpersonal Relations
Vittorio Gallese

Sequence Detector Networks and Associative Learning of Grammatical
Categories
Andreas Knoblauch and Friedemann Pulvermüller

A Distributed Model of Spatial Visual Attention
Julien Vitay, Nicolas Rougier and Frédéric Alexandre

A Hybrid Architecture using Cross-Correlation and Recurrent Neural
Networks for Acoustic Tracking in Robots
John Murray, Harry Erwin and Stefan Wermter

Image Invariant Robot Navigation Based on Self Organising Neural Place Codes
Kaustubh Chokshi, Stefan Wermter, Christo Panchev and Kevin Burn

Detecting Sequences and Understanding Language with Neural Associative
Memories and Cell Assemblies
Heiner Markert, Andreas Knoblauch and Günther Palm

Combining Visual Attention, Object Recognition and Associative
Information Processing in a NeuroBotic System
Rebecca Fay, Ulrich Kaufmann, Andreas Knoblauch, Heiner Markert and
Günther Palm

Towards Word Semantics from Multi-modal Acoustico-Motor Integration:
Application of the Bijama Model to the Setting of Action-Dependant
Phonetic Representations
Olivier Ménard, Frédéric Alexandre and Hervé Frezza-Buet

Grounding Neural Robot Language in Action
Stefan Wermter, Cornelius Weber, Mark Elshaw, Vittorio Gallese and
Friedemann Pulvermüller

A Spiking Neural Network Model of Multi-Modal Language Processing of
Robot Instructions
Christo Panchev

A Virtual Reality Platform for Modeling Cognitive Development
Hector Jasso and Jochen Triesch

Learning to Interpret Pointing Gestures: Experiments with Four-Legged
Autonomous Robots
Verena Hafner and Frédéric Kaplan

Reinforcement Learning Using a Grid Based Function Approximator
Alexander Sung, Artur Merke and Martin Riedmiller

Spatial Representation and Navigation in a Bio-inspired Robot
Denis Sheynikhovich, Ricardo Chavarriaga, Thomas Strosslin and Wulfram
Gerstner

Representations for a Complex World: Combining Distributed and Localist
Representations for Learning and Planning
Joscha Bach

MaximumOne: an Anthropomorphic Arm with Bio-Inspired Control System
Michele Folgheraiter and Giuseppina Gini

LARP, Biped Robotics Conceived as Human Modelling
Umberto Scarfogliero, Michele Folgheraiter and Giuseppina Gini

Novelty and Habituation: The Driving Force in Early Stage Learning for
Developmental Robotics
Qinggang Meng and Mark Lee

Modular Learning Schemes for Visual Robot Control
Gilles Hermann, Patrice Wira and Jean-Philippe Urban

Neural Robot Detection in RoboCup
Gerd Mayer, Ulrich Kaufmann, Gerhard Kraetzschmar and Günther Palm

A Scale Invariant Local Image Descriptor for Visual Homing
Andrew Vardy and Franz Oppacher

***************************************
Professor Stefan Wermter
Chair for Intelligent Systems
Centre for Hybrid Intelligent Systems
School of Computing and Technology
University of Sunderland
St Peters Way
Sunderland SR6 0DD
United Kingdom

email: stefan.wermter **AT** sunderland.ac.uk
http://www.his.sunderland.ac.uk/~cs0stw/
http://www.his.sunderland.ac.uk/
****************************************

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