Biomimetic Neural Learning for Intelligent Robots : Intelligent Systems, Cognitive Robotics, And Neuroscience

Wermter, Stefan; Palm, Gunther; Elshaw, Mark; International Ai-workshop on Neurobotics

ISBN 10: 3540274405 / ISBN 13: 9783540274407

Editore: Springer, 2005

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Dati bibliografici

• Titolo: Biomimetic Neural Learning for Intelligent Robots : Intelligent Systems, Cognitive Robotics, And Neuroscience
• Editore: Springer
• Data di pubblicazione: 2005
• Lingua: Inglese
• ISBN 10: 3540274405
• ISBN 13: 9783540274407
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

This book presents research performed as part of the EU project on biomimetic multimodal learning in a mirror neuron-based robot (MirrorBot) and contri- tions presented at the International AI-Workshop on NeuroBotics. The ov- all 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 robot which is inspired by nature and so performs in a more ?exible learned manner than current robots. This new type of robot 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 ofsearching for objects. As well as the researchperformed in this area for the MirrorBot project, the second part of this book incorporates signi?cant contributions from other research in the ?eld of biomimetic robotics. This second part of the book concentrates on the progress made in neuroscience inspired robotic learning approaches (in short: NeuroBotics).

Contenuti

Towards Biomimetic Neural Learning for Intelligent Robots.- Towards Biomimetic Neural Learning for Intelligent Robots.- I: Biomimetic Multimodal Learning in Neuron-Based Robots.- The Intentional Attunement Hypothesis The Mirror Neuron System and Its Role in Interpersonal Relations.- Sequence Detector Networks and Associative Learning of Grammatical Categories.- A Distributed Model of Spatial Visual Attention.- A Hybrid Architecture Using Cross-Correlation and Recurrent Neural Networks for Acoustic Tracking in Robots.- Image Invariant Robot Navigation Based on Self Organising Neural Place Codes.- Detecting Sequences and Understanding Language with Neural Associative Memories and Cell Assemblies.- Combining Visual Attention, Object Recognition and Associative Information Processing in a NeuroBotic System.- Towards Word Semantics from Multi-modal Acoustico-Motor Integration: Application of the Bijama Model to the Setting of Action-Dependant Phonetic Representations.- Grounding Neural Robot Language in Action.- A Spiking Neural Network Model of Multi-modal Language Processing of Robot Instructions.- II: Biomimetic Cognitive Behaviour in Robots.- A Virtual Reality Platform for Modeling Cognitive Development.- Learning to Interpret Pointing Gestures: Experiments with Four-Legged Autonomous Robots.- Reinforcement Learning Using a Grid Based Function Approximator.- Spatial Representation and Navigation in a Bio-inspired Robot.- Representations for a Complex World: Combining Distributed and Localist Representations for Learning and Planning.- MaximumOne: An Anthropomorphic Arm with Bio-inspired Control System.- LARP, Biped Robotics Conceived as Human Modelling.- Novelty and Habituation: The Driving Forces in Early Stage Learning for Developmental Robotics.- Modular Learning Schemes for Visual Robot Control.- Neural Robot Detection in RoboCup.- A Scale Invariant Local Image Descriptor for Visual Homing.