Department Seminar of Dr. Ayal Taitler - Model-based method for Planning and Control in Hybrid Domains
SCHOOL OF MECHANICAL ENGINEERING SEMINAR
Wednesday January 10.1.2024 at 14:00
Wolfson Building of Mechanical Engineering, Room 206
Model-based method for Planning and Control in Hybrid Domains
Dr. Ayal Taitler
a Lyon Sachs postdoctoral fellow in the Department of Mechanical and Industrial Engineering at the University of Toronto
Abstract: Robots operate in the real world, which is hybrid, i.e. comprised of continuous and discrete properties, uncertain, constrained, non-linear, and often cooperation or at least synchronization with other agents, human and robotic is required. Moreover, usually for an agent to reach its desired goal, a long time horizon is required, which accumulates errors and makes it dimensionally impossible to discretize the problem. Each of these separately poses a major challenge for autonomous behavior. Robots must be able to come up with long-term plans in the face of these challenges in order to reach autonomy. Various communities have addressed these problems; e.g., the control, automated planning, machine learning, and robotics communities, each with its own merits and weaknesses. In this work, we attempt to bridge the gap between these communities and present a unified method leveraging accurate short-term control strategy, long-term abstract planning methods, and deep neural networks tailored on the fly. Our method can handle long, continuous horizons, allowing for concurrency and synchronization, incorporation of accurate non-linear dynamic models, while balancing between expensive accurate computations and "simple" abstract computations.
Bio: Dr. Ayal Taitler is a Lyon Sachs postdoctoral fellow in the Department of Mechanical and Industrial Engineering at the University of Toronto.
Ayal's primary research focus lies in the realm of hybrid discrete-continuous problems, especially when considering (abstract/partial) models. Ayal completed his Ph.D. in the Technion Autonomous Systems and Robotics interdisciplinary program. His doctoral research focused on mixed discrete-continuous planning for autonomous robotic missions. During his Ph.D., Ayal served as the lecturer of the advanced control theory course in the Electrical and Computer Engineering faculty. Prior to his Ph.D., Ayal earned his Master's in reinforcement learning and Bachelor's degrees, both from the Faculty of Electrical and Computer Engineering at the Technion. Furthermore, Ayal has more than a decade of industrial experience within the Israeli defense sector and high-tech enterprises, working in software development and research roles.
