The Robotics and Autonomous Systems program targets students that want to engineer and build complex robotics systems that operate with various degrees of autonomy. Students will have the opportunity to learn the theory of and actually develop autonomous robotic systems in multiple domains including transportation systems, medical robotics, internet of things, smart cities, and industrial systems. The program emphasizes a holistic approach to robotics and autonomous systems including dynamics and control, perception and cognition, autonomous decision making, human-robot and robot-robot collaboration, policy and ethics.
Program Committee
David Silberberg, Program Co-Chair
Principal Professional Staff
JHU Applied Physics Laboratory
Louis L. Whitcomb, Program Co-Chair
Professor of Mechanical Engineering
Laboratory for Computational Sensing and Robotics
Johns Hopkins University
Anthony N. Johnson, Program Manager
Senior Professional Staff
JHU Applied Physics Laboratory
Courses
This course provides an in-depth exploration of artificial intelligence (AI) techniques applied to robotics. Students will gain a comprehensive understanding of the intersection between AI and robotics, covering topics such as perception, planning, control, learning algorithms including deep learning and generative models, and co-operation (swarms). Practical applications of AI in robotics will be emphasized, and students will have hands-on experience with implementing AI algorithms for robotic systems.
This course will cover the core concepts and applications of sensing systems. These include problem identification, communication, process control, types of sensors and how they work, sensor data collection techniques, data acquisition protocols, signal processing, system design (low power and mobile), machine learning, and applications including but not limited to smart health, gesture interaction, robotics, and automotive. The course is geared towards giving students direct experience in building sensing systems to act and respond (using machine learning) to information in the environment and solving programming and analytical challenges with data collected from multiple sensors. The course emphasizes an understanding of both data (using systems theory, probability, and simulation), algorithms (using synthetic and real data sets) and hardware (using IoT devices). The assignments weigh conceptual (assessments, readings, discussions, and projects) and practical (labs, problem sets) understanding equally. Prerequisites (AS.110.109 Calculus II, EN.605.206. Introduction to Programming Using Python, and some knowledge of databases and circuits)
This course permits graduate students in robotics and autonomous systems to work with a faculty mentor to explore a topic in depth or conduct research in selected areas. Requirements for completion include submission of a significant paper or project. Prerequisite(s): Seven program-applicable graduate courses including the four core courses, at least one focus area courses, and two 700-level courses. Students must also have permission of a faculty mentor, the student’s academic advisor, and the program chair
Students wishing to take a second independent study in robotics and autonomous systems should sign up for this course. Prerequisite(s): EN.665.801 Independent Study in Robotics and Autonomous Systems I and permission of a faculty mentor, the student’s academic advisor, and the program chair.