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.

The first book available on the interaction of hand-eye-brain of intelligent robots in an integrated framework Includes over 30 theoretical results and application examples of the intelligent manipulation, cognition and decision Offers an efficient maniupulation method with low-prescision sensors for the first time Offers a new manifold learning algorithm to analyze robots' sensor data in a high dimensional space

Autorentext

Hong Qiao received the B.Eng. degree in hydraulics and control and the M.Eng. degree in robotics from Xi'an Jiaotong University, Xi'an, China, the M.Phil. degree in robotics control from the Industrial Control Center, University of Strathclyde, Strathclyde, U.K. and the Ph.D. degree in robotics and artificial intelligence from De Montfort University, Leicester, U.K. in 1995. She is currently a '100-Talents Project' Professor with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. Her current research interests include pattern recognition, machine learning, bio-inspired intelligent robot, brain-like intelligence, robotics and intelligent agents. Prof. Qiao is currently a member of the Administrative Committee of the IEEE Robotics and Automation Society (RAS), the Long Range Planning Committee, the Early Career Award Nomination Committee, Most Active Technical Committee Award Nomination Committee, and Industrial Activities Board for RAS. She received Second Prize of 2014 National Natural Science Awards, First Prize of 2012 Beijing Science and Technology Award (for Fundamental Research) and Second Prize of 2015 Beijing Science and Technology Award (for Technology Inventions). She is on the Editorial Boards of five IEEE Transactions and the Editor-in-Chief of journal Assembly Automation (SCI indexed). Chao Ma received the B.S. degree in automation from Central South University, Changsha, China, in 2007, the M.S. degree and the Ph.D. degree in control science and engineering from the Harbin Institute of Technology, Harbin, China, in2010 and 2015. Currently he is an assistant professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing, P.R. China. His current research interests include intelligent robot systems, intelligent agents and robot control. Rui Li receivedthe B.Eng degree in automation engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2013. Currently he is a PhD candidate in intelligent robot with (1) the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China and (2) University of Chinese Academy of Sciences (UCAS), Beijing, China. His current research interests include intelligent robot system, high-precision assembly, compliant manipulation for robotic systems.

Inhalt
Introduction.- The Concept of Attractive Region in Environment (ARIE) and its Application in High-precision Tasks with Low-precision Systems.- The Compliance of Robotic Hands and Human-inspired Motion Model of Upper-limb with Fast Response and Learning Ability.- Learning an Intrinsic-Variable Preserving Manifold for Dynamic Visual Tracking.- Explicit Nonlinear Mapping for Manifold Learning with Neighborhood preserving polynomial embedding.- Biologically Inspired Visual Model with Memory and Association Mechanism.- Biologically Inspired Visual Model with Preliminary Cognition and Active Attention Adjustment.- Biologically Inspired Visual Cognition Model with Unsupervised Episodic and Semantic Feature Learning.- Conclusions and Future Research Directions.