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Department of Informatics
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Books and Special Issues

Biomimetic Neural Learning for Intelligent Robots

Stefan Wermter, Günther Palm, Mark Elshaw

July 2005, Springer, Heidelberg
ISBN: 3-540-27440-5, £40, 388 pp. Softcover

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 is 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 other research in the field of biomimetic robotics. This second part of the book concentrate 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 interesting and dynamic area. We would like to thank all contributors to this book, all the researchers and administrative staff within the MirrorBot project, the reviewers of the chapters and all the participants at the AI-Workshop in NeuroBotics.



Towards Biomimetic Neural Learning for Intelligent Robots (Abstract) Full Chapter (PS)
Stefan Wermter, Günther Palm, Cornelius Weber and Mark Elshaw

Biomimetic Multimodal Learning in Neuron-based Robots

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 Pulverm
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 G
ünther Palm
Combining Visual Attention, Object Recognition and Associative Information Processing in a NeuroBotic System
Rebecca Fay, Ulrich Kaufmann, Andreas Knoblauch, Heiner Markert and G
ünther 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 Pulverm
A Spiking Neural Network Model of Multi-Modal Language Processing of Robot Instructions
Christo Panchev

Biomimetic Cognitive Behaviour in Robots

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

Emergent Neural Computational Architectures based on Neuroscience can be bought from
Springer-Verlag using the booking form.



Prof. Stefan Wermter
University of Hamburg
Department of Informatics, Knowledge Technology
Vogt Koelln Str. 30
22527 Hamburg

Phone: +49 40 428 83 2434
Fax: +49 40 428 83 2515
Secretary: +49 40 428 83 2433
Email: wermter at informatik dot uni-hamburg dot de