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Deep Learning for Robotics

In this hand-on workshop, you’ll learn the fundamentals of integrating object detection into a mobile robot running on a ROS/Jetson framework. After developing your code in a Gazebo simulation environment, you’ll deploy it to the physical robot for testing.

You’ll start with an overview of the Robot Operating System(ROS) and its associated architecture. Then, you’ll build a node for simple movement of the robot using the development workflow: simulate, develop, and deploy. You’ll proceed to integrate image recognition and object detection deep neural network (DNN) models, including an exploration of how to build your own models with DIGITS. You’ll verify the robot’s behavior in simulation and finally deploy the project to a Jetson/ROS robot.

Throughout the workshop, you’ll get hands-on simulation and coding experience using a live GPU-accelerated environment. At the end, you’ll have access to additional resources to design and deploy Jetson-based applications on your own.

Course Prerequisites

Experience with deep neural networks (specifically variations of CNNs) and intermediate-level experience Python. Knowledge of Linux and C++ is helpful but not required.

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