This module guides learners through the practical steps of building a DIY mobile robot, including setting up a Raspberry Pi, configuring essential hardware and software, and integrating sensors such as LiDAR. Participants will gain hands-on experience with electronic connections, Linux installation, and advanced device configuration for robotics applications.

This module introduces essential practices for ensuring code quality and reliability in ROS 2 projects, including automated testing with GTest, integrating ROS 2 APIs into tests, and implementing continuous integration and deployment pipelines. Learners will also discover how to use status badges to monitor project health and streamline collaborative development.

This module introduces learners to integrating large language models (LLMs) with ROS 2 to build intelligent AI agents for robotics applications. You will explore the architecture, setup, and practical use cases of ROS 2 AI agents, including hands-on examples with custom tools and MoveIt2 integration. By the end, you'll understand how LLMs can enhance robotic reasoning and control.

This module introduces the integration of deep reinforcement learning algorithms with ROS 2 for robotic applications. Learners will explore value-based methods, set up simulation environments using Isaac Lab, and practice training and testing robotic navigation tasks. By the end, participants will gain hands-on experience deploying and evaluating RL models in simulated robotics scenarios.

This module guides learners through the process of developing and integrating visualization and simulation plugins within the ROS 2 ecosystem. Participants will explore plugin architecture, implement C++ source code, configure XML files, and compile plugins for tools like RQT and Gazebo. By the end, learners will understand how to extend ROS 2 functionality with custom plugins.