The Robotics MSE program at the University of Pennsylvania is a multi-disciplinary program jointly sponsored by the Departments of Computer and Information Science, Electrical and Systems Engineering, and Mechanical Engineering and Applied Mechanics. Housed and administered by the GRASP Lab, one of the top robotics research centers in the world, Penn’s ROBO master’s program educates students in the science and technology of robotics, vision, perception, control, automation, and machine learning. Our program provides an ideal foundation for jobs in a variety of industries including robotics, aerospace, automotive, industrial automation and defense; it also provides a solid basis for further graduate studies.
Our faculty members are dedicated to building up the next generation of engineers. In addition to being incredible mentors, they’re leading experts and researchers in their fields.
This course presents the fundamental kinematic, dynamic, and computational principles underlying most modern robotic systems. The main topics of the course include: rotation matrices, homogeneous transformations, manipulator forward kinematics, manipulator inverse kinematics, Jacobians, path and trajectory planning, sensing and actuation, and feedback control. The material is reinforced with hands-on lab exercises involving a robotic arm.
An introduction to the problems of computer vision and other forms of machine perception that can be solved using geometrical approaches rather than statistical methods. This course is designed to provide students with an exposure to the fundamental mathematical and algorithmic techniques that are used to tackle challenging image based modeling problems. The subject matter of this course finds application in the fields of Computer Vision, Computer Graphics and Robotics. This course will also explore various approaches to object recognition that make use of geometric techniques, these would include alignment based methods and techniques that exploit geometric invariants.
This course will cover the mathematical fundamentals and applications of machine learning algorithms to mobile robotics. Possible topics that will be discussed include probalistic generative models for sensory feature learning. Bayesian filtering for localization and mapping, dimensionality reduction techniques for motor control, and reinforcement learning of behaviors.
