The “Hand-eye-brain” System of Intelligent Robot: From Interdisciplinary Perspective of Information Science and Neuroscience (Research on Intelligent Manufacturing)

This book reports the new results of intelligent robot with hand-eye-brain, from the interdisciplinary perspective of information science and neuroscience. It collects novel research ideas on attractive region in environment (ARIE), intrinsic variable preserving manifold learning (IVPML) and biologically inspired visual congnition, which are theoretically important but challenging to develop the intelligent robot. Furthermore, the book offers new thoughts on the possible future development of human-inspired robotics, with vivid illustrations. The book is useful for researchers, R & D engineers and graduate students working on intelligent robots Preface Contents Part I Introduction 1 Background References 2 Motivations References 3 Structure of the Book Part II The Hand of Robots–Intelligent Manipulation Theory and Application of Robots 4 The Development and Challenges of Robot Manipulation References 5 The Concept of ``Attractive Region in Environment (ARIE)'' and Its Application in High-Precision Tasks with Low-Precision Systems 5.1 General Concept of ``Attractive Region in Environment'' 5.2 General Formulation of ``Attractive Region in Environment'' 5.3 Extension of Attractive Region in High Dimensional Space 5.4 Applications of ``Attractive Region in Environment'' in Industrial Manufacturing 5.5 Conclusions References 6 The Compliance of Robotic Hands and Human-Inspired Motion Model of Upper-Limb with Fast Response and Learning Ability 6.1 The Combination of Compliance and Robotic Hands from Functionality to Mechanism 6.2 Forward Dynamics of the Upper Limb 6.3 Movement Control Method Based on the Patterns 6.4 Experiment Results 6.5 Conclusions References Part III The Eye of Robots–Intelligent Perception Theory and Application of Robots 7 The Development and Challenges of Robot Perception References 8 Learning an Intrinsic-Variable Preserving Manifold for Dynamic Visual Tracking 8.1 Intrinsic Variable Preserving Manifold Learning (IVPML) 8.2 Visual-Head-Tracking Algorithm with Complicated Backgrounds Using IVPML 8.3 Experiment Results 8.4 Conclusions References 9 Explicit Nonlinear Mapping for Manifold Learning with Neighborhood Preserving Polynomial Embedding 9.1 Explicit Nonlinear Manifold Learning Model 9.2 Neighborhood Preserving Polynomial Embedding (NPPE) Algorithm 9.3 Experiment Results 9.4 Conclusions References Part IV The Brain of Robots–Intelligent Cognition Theory and Application of Robots 10 The Development and Challenges of Robot Cognition 11 Biologically Inspired Visual Model with Memory and Association Mechanism 11.1 Biological Evidences for Memory and Association Mechanism 11.2 Visual Model with Memory and Association Mechanism 11.2.1 Familiarity Discrimination 11.3 Experiment Results 11.4 Conclusions References 12 Biologically Inspired Visual Model with Preliminary Cognition and Active Attention Adjustment 12.1 Biological Evidences for Preliminary Cognition and Active Attention Adjustment 12.2 Visual Model with Preliminary Cognition and Active Attention Adjustment 12.3 Experiment Results 12.4 Conclusions References 13 Biologically Inspired Visual Cognition Model with Unsupervised Episodic and Semantic Feature Learning 13.1 Biological Evidences for Human Visual Cognition 13.2 Visual Model with Unsupervised Episodic and Semantic Feature Learning 13.3 Experiment Results 13.4 Conclusions References Part V Conclusions and Future Research Directions 14 Conclusions