Robot Learning By Visual Observation
by Visual Observation /
2017 / English / PDF
10.6 MB Download
by Aleksandar Vakanski (Author), Farrokh Janabi-Sharifi (Author) This book presents programming by demonstration for robot learning from observations with a focus on the trajectory level of task abstraction Discusses methods for optimization of task reproduction, such as reformulation of task planning as a constrained optimization problem Focuses on regression approaches, such as Gaussian mixture regression, spline regression, and locally weighted regression Concentrates on the use of vision sensors for capturing motions and actions during task demonstration by a human task expert In times of a growing worldwide demand for automation and robotics applications, as well as an aging population and a shrinking work force, the development of robots with capacity to learn by observation and abilities for visual perception of the environment with vision sensors emerges as an important means to mitigate the above problems. This book is a valuable reference for university professors, graduate students, robotics enthusiasts, and companies that seek to develop robots with such abilities. is a Clinical Assistant Professor in Industrial Technology at the University of Idaho, Idaho Falls. He received a Ph.D. degree from the Department of Mechanical and Industrial Engineering at Ryerson University, Toronto, Canada, in 2013. The scope of his research interests encompasses the fields of robotics and mechatronics, artificial intelligence, computer vision, and control systems. is a Professor of Mechanical and Industrial Engineering and the Director of Robotics, Mechatronics and Manufacturing Automation Laboratory (RMAL) at Ryerson University, Toronto, Canada. He is currently an Associate Editor of The International Journal of Optomechatronics, and an Editorial Member of The Journal of Robotics and The Open Cybernetics and Systematics Journal. His research interests include optomechatronic systems with the focus on image-guided control and planning.
