Program Overview

MRSD Program Curriculum

The Master of Science in Robotic Systems Development (MRSD) Program at Carnegie Mellon University requires students to complete 183 units of coursework to be eligible for graduation. The MRSD curriculum includes four semesters of coursework and a summer internship, resulting in a 21-month program.

Semester 1 (Fall)

• Course: 16-642
  • Title: Manipulation, Estimation, & Control
  • Units: 12
• Course: 16-650
  • Title: Systems Engineering and Management for Robotics
  • Units: 12
• Course: 16-665
  • Title: Robot Mobility on Air, Land, & Sea
  • Units: 12
• Course: 16-720
  • Title: Computer Vision (or Technical Elective)
  • Units: 12

Semester 2 (Spring)

• Course: 16-662
  • Title: Robot Autonomy
  • Units: 12
• Course: 16-xxx
  • Title: Technical Elective (or 16-720)
  • Units: 12
• Course: 16-681
  • Title: MRSD Project I
  • Units: 15
• Course: 16-697
  • Title: Introduction to Robotics Business
  • Units: 9

Summer Semester

• Course: 16-991
  • Title: Internship
  • Units: 3

Semester 3 (Fall)

• Course: 16-682
  • Title: MRSD Project II
  • Units: 15
• Course: 16-698
  • Title: Advanced Topics in Robotics Business
  • Units: 9
• Course: xx-xxx
  • Title: Technical Elective
  • Units: 12
• Course: xx-xxx
  • Title: Business Elective
  • Units: 6

Semester 4 (Spring)

• Course: xx-xxx
  • Title: Technical Elective
  • Units: 12
• Course: xx-xxx
  • Title: Technical Elective
  • Units: 12
• Course: xx-xxx
  • Title: Technical Elective
  • Units: 12
• Course: xx-xxx
  • Title: Business Elective
  • Units: 6

Internship

MRSD students complete a 12-week internship in the summer between the first and 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 Electives

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 total 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)

Pre-approved Technical Electives

The following technical electives are pre-approved for the MRSD Program and do not require permission from the program administration:

• 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