Robotics Minor
The transcript-recognized undergraduate academic Robotics Minor must be completed in conjunction with an undergraduate degree at The University of Texas at Austin in one of the following majors:
- Aerospace Engineering
- Electrical and Computer Engineering
- Computational Engineering
- Mechanical Engineering
- Computer Science
The minor is administered by Texas Robotics as a collaboration between the Cockrell School of Engineering and the College of Natural Sciences. Details about the minor in robotics are available at https://robotics.utexas.edu/.
Admissions
To be considered for admissions into the Robotics Minor, students must meet the following requirements:
- The minor must be completed in conjunction with an undergraduate degree in one of the following supported majors of Computer Science, Aerospace Engineering, Electrical and Computer Engineering, Computational Engineering, or Mechanical Engineering.
- Students who have completed 24 hours or more in residence will be encouraged to apply online at the earliest possible date. Applications open every spring semester.
Total Hours Required: 15
Requirements
Requirements are listed in the table below. Additional requirements may follow the table, so be sure to read the entire page. Some required courses listed below may also satisfy General Education requirements, including Core Curriculum. Students should also review the University's Undergraduate Minor and Certificate Requirements and Policies.
| Code | Title | Hours |
|---|---|---|
| RBT 350 | Gateway to Robotics | 3 |
| Content Area Courses | ||
| Select one course from at least four different content areas: | 12 | |
| Hardware Courses: | ||
| Advanced Mechatronics I | ||
| Advanced Mechatronics II | ||
| Robot Mechanism Design | ||
| Programming Courses: | ||
| Application Programming for Engineers | ||
| F1/10 Autonomous Driving | ||
| Embedded Systems Design Laboratory | ||
| Embedded and Real-Time Systems Laboratory | ||
| Aerial Robotics | ||
| Modeling and Control Courses: | ||
| Automatic Control System Design | ||
| Feedback Control Systems | ||
| Introduction to Automatic Control | ||
| Linear System Analysis | ||
| Biomechanics of Human Movement | ||
| Robotics and Automation | ||
| F1/10 Autonomous Driving | ||
| Sensing, Perception, and Planning Courses: | ||
| F1/10 Autonomous Driving | ||
| Robotics and Automation | ||
| Aerial Robotics | ||
| Embedded Systems Design Laboratory | ||
| Embedded and Real-Time Systems Laboratory | ||
| Neural Engineering | ||
| Computer Vision | ||
| Introduction to Computer Vision | ||
| Machine Learning Courses: | ||
| Neural Networks | ||
| Artificial Intelligence | ||
| Principles of Machine Learning I | ||
| Introduction to Machine Learning and Data Sciences | ||
| Data Science Laboratory | ||
| Machine Learning and Data Analytics for Edge Artificial Intelligence | ||
| Neural Engineering | ||
| Data Science Principles | ||
| Total Hours | 15 | |
Additional Requirements and Policy
- All students will be required to take a three-credit-hour, gateway course (RBT 350 ) that will prepare students to take robotics minor courses in areas outside of their declared major.
- In addition to the gateway course, students must take 4 courses; each course must be from a different content area. There are five content areas: hardware; programming; modeling and control; sensing, perception and planning; and machine learning. See the course list for each content area above.
- All classes must be taken on the letter-grade basis. The student must earn a combined grade point average of at least 2.00 in these courses.