MRSD Program Curriculum

Source: https://mrsd.ri.cmu.edu/curriculum/ Parent: https://mrsd.ri.cmu.edu/

CurriculumAlex Krause2026-01-26T14:03:41-05:00

Students in the Master of Science in Robotic Systems Development (MRSD) Program at Carnegie Mellon University must complete 183 units of coursework, as dictated by the curriculum, to be eligible for graduation. The MRSD curriculum includes four semesters of coursework and a summer internship, resulting in a 21-month program. The suggested sequence of courses is outlined below. Detailed course descriptions can be accessed through the Schedule of Classes. Examples of projects developed by previous teams of students for the MRSD Project Course and Robot Autonomy can be found here.

Semester 1 (Fall)		48 units
Course	Title	Units
16-650	Systems Engineering and Management for Robotics	12
16-662	Robot Autonomy	12
16-665	Robot Mobility on Air, Land, & Sea	12
16-720	Computer Vision (or Technical Elective)	12

Semester 2 (Spring)		48 units
Course	Title	Units
16-642	Manipulation, Estimation, & Control	12
16-xxx	Technical Elective (or 16-720)	12
16-681	MRSD Project I	15
16-697	Introduction to Robotics Business	9

Summer Semester
Course	Title	Units
16-991	Internship	3

Semester 3 (Fall)		42 units
Course	Title	Units
16-682	MRSD Project II	15
16-698	Advanced Topics in Robotics Business	9
xx-xxx	Technical Elective	12
xx-xxx	Business Elective	6

Semester 4 (Spring)		42 units
Course	Title	Units
xx-xxx	Technical Elective	12
xx-xxx	Technical Elective	12
xx-xxx	Technical Elective	12
xx-xxx	Business Elective	6

16-991 Internship

MRSD students complete a 12-week internship in the summer between the first & second academic year. Internships are to fall within the summer term as outlined by the University Academic Calendar. Interns are required to submit a final end-of-internship report documenting the work that they carried out as part of their internship. The MRSD Program Director reviews the reports and assigns a Pass/Fail grade at the end of the summer semester.

Business Elective(s)

Students are required to complete a total of 12 units of Business Electives to be eligible for graduation. Business Electives are to be chosen from the Heinz College, though options from the Tepper School of Business will also be accepted. Many of the courses offered by Tepper and Heinz are “mini” courses. Mini courses are 6 units and last one-half of a semester. Students will need to complete either one 12-unit course or two 6-unit mini courses to meet the Business Elective requirement.

Technical Electives

Students must complete a total of 60 units of approved Technical Electives to be eligible for graduation. This requirement is met through five 12-unit courses. Students are permitted to take up to 12 units of advanced undergraduate-level (i.e. xx-300/xx-400) elective coursework with program approval.

Students must enroll for a minimum of 4 Technical Electives offered by the School of Computer Science (SCS).
2 of the SCS Technical Electives must be pre-approved courses from The Robotics Institute (16-xxx)
The 2 remaining SCS Technical Electives must be pre-approved courses from any SCS Department (02-xxx, 05-xxx, 08-xxx, 10-xxx, 11-xxx, 15-xxx, 16-xxx, 17-xxx)
A maximum of 1 Technical Elective may be a pre-approved course from the College of Engineering (06-xxx, 12-xxx, 18-xxx, 19-xxx, 24-xxx, 27-xxx, 39-xxx, 42-xxx).

The technical electives listed below are pre-approved for the MRSD Program and do not require permission from the program administration. If you find an MRSD-relevant course that is not included on this list, please send the course name, number and description to the MRSD Program Manager for review and approval. Also include your reasoning for requesting that specific course.

Pre-approved Technical Electives:

05-833 – Gadgets, Sensors and Activity Recognition in HCI
05-834 – Applied Machine Learning
05-891 – Designing Human Centered Software
10-601 – Machine Learning
10-623 – Generative AI
10-703 – Deep Reinforcement Learning & Control
10-707 – Advanced Deep Learning
10-708 – Probabilistic Graphical Models
10-714 – Deep Learning Systems: Algorithms and Implementation
10-725 – Convex Optimization
11-601 – Coding & Algorithms Bootcamp
11-611 – Natural Language Processing
11-642 – Search Engines
11-663 – Applied Machine Learning
11-755/18-797 – Machine Learning for Signal Processing
11-767 – On-Device Machine Learning
11-777 – Advanced Multimodal Machine Learning
11-785 – Introduction to Deep Learning
15-122 – Principles of Imperative Computation
15-513 – Introduction to Computer Systems
15-615 – Database Applications
15-619 – Cloud Computing
15-624 – Foundations of Cyber-Physical Systems
15-640 – Distributed Systems
15-650 – Algorithms and Advanced Data Structures
15-651 – Algorithm Design and Analysis
15-662 – Computer Graphics
15-663 – Computational Photography
15-780 – Artificial Intelligence
15-821 – Mobile and Pervasive Computing
15-887 – Planning Execution and Learning
16-467 – Human Robot Interaction
16-623 – Designing Computer Vision Apps
16-663 – F1Tenth Autonomous Racing
16-664 – Self-Driving Cars
16-667 – Autonomous Air Vehicle Design and Development
16-711 – Kinematics, Dynamic Systems and Control
16-722 – Sensing and Sensors
16-725 – Methods in Medical Image Analysis
16-726 – Learning-based Image Synthesis
16-735 – Ethics and Robotics
16-740 – Learning for Manipulation
16-741 – Mechanics of Manipulation
16-745 – Optimal Control and Reinforcement Learning
16-748 – Underactuated Robots
16-761 – Mobile Robots
16-762 – Mobile Manipulation
16-778 – Mechatronic Design
16-782 – Planning and Decision-making in Robotics
16-785 – Integrated Intelligence in Robotics: Language, Vision, and Planning
16-791 – Applied Data Science
16-811 – Mathematical Fundamentals for Robotics
16-822 – Geometry-based Methods in Vision
16-823 – Physics-based Methods in Vision (Appearance Modeling)
16-824 – Visual Learning and Recognition
16-825 – Learning for 3D Vision
16-831 – Introduction to Robot Learning
16-832 – Integrated Planning and Learning
16-833 – Robot Localization and Mapping
16-843 – Manipulation Algorithms
16-848 – Hands: Design and Control for Dexterous Manipulation
16-861 – Mobile Robot Development (project course)
16-865 – Space Robotics Development
16-867 – Human Robot Interaction
16-868 – Biomechanics & Motor Control
16-880 – Engineering Haptic Interfaces
16-882 – Special Topics in Systems Engineering and Project Management for Robotics
16-886 – Embodied AI Safety
16-887 – Special Topics: Robotic Caregivers and Intelligent Physical Collaboration
16-891 – Multi-Robot Planning and Coordination
16-899 – Section C: Adaptive Control and Reinforcement Learning
16-899 – Section D: Nuclear Robots
17-630 – Data Structures and Algorithms for Engineers
17-653 – Managing Software Development
17-655 – Architectures for Software Systems
18-642 – Embedded System Software Engineering
18-648 – Embedded Real-Time Systems
18-649 – Distributed Embedded Systems
18-660 – Optimization
18-698 – Neural Signal Processing
18-745 – Rapid Prototyping of Computer Systems
18-777 – Complex Large-Scale Dynamic Systems
24-614 – Microelectromechanical Systems
24-651 – Material Selection for Mechanical Engineers
24-671 – Special Topics: Electromechanical Systems Design
24-672 – Special Topics in DIY Design and Fabrication
24-673 – Soft Robots: Mechanics, Design and Modeling
24-674 – Design of Biomechatronic Systems for Humans
24-683 – Design for Manufacture and the Environment
24-776/18-776 – Non-Linear Controls
24-788 – Machine Learning and Artificial Intelligence for Engineers
39-648 – Rapid Design and Prototyping of Computer Science