Robotics and Autonomy MSRA
Degree Awarded: Master of Science in Robotics and Autonomy (MSRA)
Minimum Required Credits: 30.0
Co-op Option: Available for full-time, on-campus master's-level students
Classification of Instructional Programs (CIP) code: 14.4201
Standard Occupational Classification (SOC) code: 11-9041
About the Program
The Master of Science in Robotics and Autonomy delivers a rigorous, interdisciplinary curriculum designed to cultivate technical leadership in the engineering of intelligent systems. By integrating hardware design with advanced computational intelligence, the program enables students to bridge the gap between theoretical autonomy and physical deployment. Students develop:
- Expertise in machine learning and robotics fundamentals, including process instrumentation, control for mechatronic systems, and decision-making for autonomous agents.
- Technical proficiency in designing, building, programming, and evaluating complex robotic systems.
- Critical thinking and creativity to innovate solutions for diverse societal and industrial challenges.
- Skill in integrating industry-leading tools for automation, precision manufacturing, and systems integration.
- Specialized knowledge in high-impact domains such as Autonomous Systems, Human-Robot Interaction (HRI), Medical Robotics, or Intelligent Manufacturing.
- Graduates emerge with the advanced technical capabilities and holistic systems-thinking required to innovate across a broad spectrum of critical infrastructure and application areas.
The program offers a comprehensive and flexible curriculum tailored to individual career goals through consultation with an academic advisor. Students complete 30.0 credits, combining core foundational courses with a selected subject-area focus.
The curriculum is structured to provide a balance of theoretical depth and practical application through technical electives. The MS program is designed so that a student may complete the degree requirements in less than 2 years of full-time study.
Full-time on-campus students are eligible for the Graduate Co-op Program, a distinctive experience that integrates academic learning with a 6-month, full-time professional placement. This hands-on opportunity allows students to apply classroom knowledge in practical settings and gain meaningful industry experience before graduation.
Graduates are well-prepared for:
- Advanced leadership roles in the development and design of robotic systems across industries such as medicine, healthcare, large-scale retail, and advanced manufacturing.
- Careers driving the incorporation of Industry 4.0, the Internet of Things (IoT), and automation in global supply chains and production environments.
- Entrepreneurial ventures and the development of innovative robotic technologies.
- Pursuit of doctoral study in robotics or related engineering and computing disciplines.
The program's focus on interdisciplinary technical skills and practical application provides the professional foundation necessary for career growth in the rapidly advancing field of autonomy.
Drexel's MS in Robotics and Autonomy stands out for its interdisciplinary, flexible, and career-focused approach. Students benefit from:
- A Unified Academic Environment: A program that draws from the diverse expertise within the College of Engineering and Computing, reflecting the interdisciplinary nature of modern robotics.
- Customizable Specializations: The ability to focus studies on high-impact areas like Medical Robotics, Biomechanics, Reinforcement Learning, and Computer Vision.
- Integration of Experiential Learning: Access to the signature 6-month graduate co-op, connecting classroom theory to industrial practice.
- Industry Alignment: A curriculum specifically designed to address advancements in robotics and their real-world applications in society and industry.
Additional Information
For more information about this program, please contact the School of Engineering
Admission Requirements
Applicants must satisfy general requirements for graduate admission including a minimum 3.0 GPA (on a 4.0 scale) for the last two years of undergraduate studies, as well as for any subsequent graduate work, and hold a bachelor's degree in an engineering discipline from an accredited college or university. Preferred bachelor's programs include electrical engineering, computer engineering, mechanical engineering, and computer science. An undergraduate degree earned abroad must be deemed equivalent to a US bachelor's.
The GRE general test is optional for all applicants. TOEFL, IELTS, PTE, or Duolingo is required if the language of instruction of your previous degree was not English.
For additional information on how to apply and deadlines, visit the College of Engineering's Admission Guidelines or Drexel's admissions page for Robotics and Autonomy.
Degree Requirements
| Core/Foundational Courses | ||
| ECE 5708 | Decision-Making for Robotics | 3.0 |
| MEM 5001 | Applied Engineering Mathematics I | 3.0 |
| MEM 5870 | Foundations of Robotics Dynamics and Control | 3.0 |
| MEM 5871 | Foundations of Robotics Engineering | 3.0 |
| Subject-Area Focused Courses | ||
| Select three courses from one of the following focus areas: | 9.0 | |
| Autonomous Systems and Design | ||
| Reinforcement Learning | ||
| Pattern Recognition | ||
| Applied Robotics Laboratory | ||
| Fundamentals of Systems I | ||
| Cognition and Human-Robot Interaction (HRI) | ||
| Principles of Artificial Intelligence | ||
| Reinforcement Learning | ||
| Computer Vision | ||
| Embodied AI | ||
| Data Analysis and Machine Learning for Science and Manufacturing | ||
| Medical Robotics and Biomechanics | ||
| Joint Biomechanics and Modeling | ||
| Medical Device Development | ||
| Medical Technology Innovation | ||
| Medical Robotics | ||
| Robotics Engineering for Healthcare | ||
| Robotics for Intelligent Manufacturing | ||
| Computer Vision | ||
| Pattern Recognition | ||
| Industrial Advanced Robotics and Mechatronics | ||
| Precision Manufacturing | ||
| Nondestructive Evaluation Methods | ||
| Systems Integration and Test | ||
| Robotics for Autonomous and Unmanned Systems | ||
| Advanced Control Systems I | ||
| Advanced Dynamics I | ||
| Advanced Control Systems II | ||
| Advanced Dynamics II | ||
| Aircraft Flight Dynamics and Control | ||
| Electives | ||
| Select three of the following courses: | 9.0 | |
| Neuroimaging and Brain Computer Interfaces | ||
| Programming Data Structures and Algorithms | ||
| Systems Basics | ||
| Fundamental Mathematics for Computer Science | ||
| Neuro-symbolic Collective Intelligence | ||
| Probability and Random Variables | ||
| Deterministic Signal Processing | ||
| Image Processing | ||
| Fundamentals of Systems II | ||
| Process Instrumentation and Control for Mechatronic Systems | ||
| Applied Engineering Mathematics II | ||
| AI and ML Methods in Engineering Mathematics | ||
| Robust Control Systems | ||
| Applied Optimal Control | ||
| Theory of Nonlinear Control | ||
Select any untaken Subject-Area Focus courses from the list above | ||
| Optional Co-op Experience | ||
| Co-op is an option for this degree for full-time on-campus students. Students choosing this option will be required to complete COOP 5000 as preparation for their co-op experience. | 0.0 | |
| Total Credits | 30.0 | |