Carnegie Mellon University

Carnegie Mellon Robotics Academy

Use educational affordances of robotics to create CS-STEM opportunities for all learners

Carnegie Mellon Robotics Academy Training for Jetson Nano with JetBot AI (On Demand)

In this face-to-face teacher training, you will work with the world leaders in robotics education to learn how to teach Artificial Intelligence in a robotics context using the NVIDIA Jetson Nano and JetBot platform. Carnegie Mellon Robotics Academy's specialized training program for the NVIDIA Jetson Nano and JetBot AI has been specifically designed for high school and college educators who wish to infuse their curriculum with an innovative and practical approach to teaching artificial intelligence (AI) and mobile robotics. 

Why AI and Mobile Robotics? AI is transforming industries from healthcare to finance, agriculture to entertainment, and many more. Concurrently, mobile robotics is evolving at a rapid pace, with robots playing increasingly significant roles in manufacturing, logistics, healthcare, and even personal assistance. In this ever-changing landscape, there is a pressing need for students to understand AI and robotics from a fundamental level. 

Value of Our Training Our comprehensive training program provides educators with the knowledge and tools to successfully teach AI and mobile robotics using the Nvidia Jetson Nano on the Jetbot robotics platform. The Jetson Nano is a small, powerful computer designed specifically for AI and machine learning, making it an ideal tool for teaching these subjects. The Jetbot, a mobile robot powered by the Jetson Nano, provides a hands-on, tangible application of AI, making it a perfect companion tool.

jetbot_city.jpg jetbot_fov.png

Our training program includes:

  • Introduction to AI, machine learning, and robotics fundamentals
  • Hands-on experience with the Nvidia Jetson Nano and Jetbot AI
  • Strategies to effectively teach these complex subjects in an engaging and accessible way
  • Continued support and access to classroom teaching materials

Upon completion of the training, participants will receive a certificate of completion stating 36 professional development hours in areas including artificial intelligence, robotics, and coding.

Alignment with Standards

The Jetson Nano with JetBot AI training course is aligned to the 5 Big Ideas in AI (Perception, Representation & Reasoning, Learning, Natural Interaction, and Societal Impact) and K-12 AI Guidelines defined by


Topic 1: Getting Started with Jetson Nano

Getting Started:
- What is the Jetson Nano?
- Features and Applications
- Setting up the Jetson Nano

Machine Vision:
- Image Classification
- Object Detection

Topic 2: Basic Motion with the Jetson Nano

Basic Motion:
- Mobile Robotics
- Jetbot Platform Assembly
- Basic Motion Jupyter Notebook
- Moving Forward, Backward, Left, and Right
- Behavior-Based Programming & Pseudocode
- Python Programming
- Basic Motion Challenges
- Basic Motion Interactive Control

Topic 3: Teleoperation with Jetson Nano

- What is Teleoperation? 
- What is the Internet of Things (IoT)?
- Jetson Nano and IoT
- Teleoperation Configuration
- Teleoperation Jupyter Notebook
- Teleoperation Practice Challenges

- Configuring Remote Camera Feed

- Cybersecurity Threats in Robotics
- Understanding AIoT (Artificial Intelligence of Things)
- Defense in Depth

Topic 4: GPIO (LED & Bumper) on Jetson Nano

GPIO (General Purpose Input & Output):
- Introduction to GPIO on Jetson Nano
- GPIO Software Setup
- Introduction to LEDs
- Controlling LEDs
- Introduction to Bumper Switches
- Bumper Switches
- Python Programming
- GPIO Practice Challenges

Topic 5: Collision Avoidance with Jetson Nano

Collision Avoidance:

- What is Collision Avoidance?
- Collision Avoidance on Jetbot
- Virtual Safety Bubble
- Collision Avoidance Jupyter Notebook
- Obstacle Data Collection

Training the Road Follower Model:
- Road Follower: RESnet-18
- Road Following Video Feed (TensorRT)
- Road Following (TensorRT) Model Optimization
- Troubleshooting Road Following
- Road Following Practice Challenges

Topic 6: Path Following with Jetson Nano

Path Following:
- What is Path Following?
- Path Following on Jetbot
- Path Following Jupyter Notebook
- Path Data Collection

Training the Neural Network
- Convolutional Neural Networks (CNNs): RESnet-18
- Training the RESnet-18 model
- Model Optimization for Jetson Nano
- Running Path Following
- Troubleshooting Path Following
- Path Following Practice Challenges

Topic 7: Road Following + Collision Avoidance with Jetson Nano

Road Following + Collision Avoidance:
- What is Road Following with Collision Avoidance?
- Road Following with Collision Avoidance Jupyter Notebook
- Road Following with Collision Avoidance Practice Challenges

Topic 8: Autonomous Racing with Jetson Nano

Autonomous Racing:
- Introduction to Reinforcement Learning
- Components of Reinforcement Learning
- Training the Reinforcement Learning Agent
- Real-World Applications
- Reinforcement Learning Game
- Reinforcement Learning with the Jetbot
- Autonomous Racing with Reinforcement Learning

Topic 9: AprilTag Navigation with Jetson Nanoapriltag_picture.png

AprilTag Navigation:
- Introduction to AprilTags
- Introduction to Localization
- Introduction to ROS (Robot Operating System)
- Camera Calibration
- AprilTag Navigation

Robot Hardware and Software

The following is required in order to complete the GPIO module:

  • Small Breadboard, Male-to-Female Prototyping Jumper Cables, Male-to-Male Prototyping Jumper cables, 1x 1k Ohm resistor, 1x 200 Ohm resistor, 1x LED, 1x Bumper Sensor

The following is recommended in order to take this course:

Other Materials

Upon Completion

  • Certificate of Completion
    • May be used to apply for Continuing Education Credits
    • ACT 48 credits / 36 hours per class (for Pennsylvania teachers only)


Class Eligibility

Classes at the Carnegie Mellon Robotics Academy are available to individuals who are at least 18 years of age to enroll. The Carnegie Mellon Robotics Academy reserves the right to restrict, suspend or terminate any student for violation of these policies. In consideration of your involvement with the Carnegie Mellon Robotics Academy, you agree to provide true, accurate and current information about yourself when you register. If you provide any information that is inaccurate or if the Carnegie Mellon Robotics Academy has reasonable grounds to suspect the information is inaccurate, the Carnegie Mellon Robotics Academy has the right to terminate your account.


Purchasing a seat gives access to one participant only. Resources distributed as part of the class are for use of the participant only. Purchase Orders are also acceptable. Please contact the Carnegie Mellon Robotics Academy at for information about registering for the course and payment for the course. Please email if you have questions about the content of the course.


The Carnegie Mellon Robotics Academy will offer partial refunds for tuition expenses only if class registrants contact us prior to the class filling up. To be fair to the registrants in the sessions, we cannot give refunds once a class is full.

Copyrighted Class Material

All course documents are owned by the Carnegie Mellon Robotics Academy. These materials may not be reprinted in any form except those specified for instructional purposes. The course documents and presentations may be displayed and printed for personal, non-commercial use only. Only students registered for this course may access this material. The Carnegie Mellon Robotics Academy makes every effort to provide accurate and up-to-date content. However, we have no liability for the accuracy, content, or accessibility of the hyperlinks included with class material.

Use of Student Material

The Carnegie Mellon Robotics Academy reserves the right to use coursework done by students for the purpose of advancing the educational mission of the Academy. When this occurs, students will be given the option to have their name credited to the material. This includes, but is not limited to, text, graphics, multimedia and other material created as part of the Carnegie Mellon Robotics Academy online course assignments.


You agree to indemnify and hold the Carnegie Mellon Robotics Academy harmless from any and all losses, actions, controversies, suits, demands, claims, liabilities or any causes of action whatsoever. You expressly agree that the Carnegie Mellon Robotics Academy is not responsible or liable for any infringement of another’s rights, including intellectual property rights.

Note: Policies are subjected to occasional revisions.