Robot Learning by Visual Observation

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

ALEKSANDAR VAKANSKI is a Clinical Assistant Professor in Industrial Technology at the University of Idaho, Idaho Falls, USA. 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.

FARROKH JANABI-SHARIFI is a Professor of Mechanical and Industrial Engineering and the Director of Robotics, Mechatronics and Automation Laboratory (RMAL) at Ryerson University, Toronto, Canada. He is currently a Technical Editor of IEEE/ASME Transactions on Mechatronics, 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.