Nanodegree Program
Become A Robotics Software Engineer
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Get Notified
Get notified when the Robotics Software Engineer program launches.
Thanks for your interest!
We'll be in touch soon.
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Time
Two 4-month terms
Study 15 hrs/week and complete in 8 mo.
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Classroom Opens
May 15, 2018
Classroom opens in 20 days.
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Prerequisites
Mathematics & Programming
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Student Rating
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Estimated Salary
Based on US job data
Prerequisites and Requirements
To succeed in this program, you need to have significant experience with:
- Calculus and Linear Algebra
- Statistics and Probability
- Intermediate Python
- Unix/Linux Command Line Basics
- Basic Physics (Newtonian Mechanics)
Background in the following is recommended but not required:
- Intermediate C++
- programming for ROS
- Machine Learning
Looking to refresh your skills or prepare now? Get started with the following resources:
In Collaboration With
Why Take This Nanodegree Program?
In this program, you’ll gain hands-on experience developing robotics solutions as you cover topics such as: Robot Operating System (ROS), Kinematics, Control, Simultaneous Localization and Mapping (SLAM), and more. You’ll learn cutting-edge techniques like Deep Reinforcement Learning through our partnership with NVIDIA's Deep Learning Institute. You’ll master the key skills necessary to become a software engineer in the transformational field of robotics and applied artificial intelligence.
World-Class Curriculum Partners
Udacity has joined forces with NVIDIA and Electric Movement to create a groundbreaking learning experience that features world-class curriculum. You’ll master cutting-edge skills and techniques, gain hands-on experience, and build unique projects focused on the most important concepts and topics in the field of robotics.
Career-Ready Skills
Graduates of this program will emerge fully prepared to join innovative robotics teams, and develop pioneering robotics solutions. You’ll be perfectly positioned to take advantage of rapidly-increasing demand for robotics talent, and the projects you build in this program will become part of a portfolio that demonstrates your mastery of career-ready robotics skills.
NVIDIA Jetson TX2
The World’s Leading Embedded AI Computing Platform
Through our partnership with NVIDIA, eligible Udacity students entering Term 2 will receive an education discount on the NVIDIA Jetson TX2 Developer Kit. This represents an opportunity to integrate unparalleled embedded compute capability into the learning experience.
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NVIDIA Jetson TX2 Developer Kit Details
With its unrivaled embedded compute capability, the Jetson TX2 represents a revolutionary new era for smart embedded robotics. By enabling server-class AI compute performance for edge devices, the Jetson TX2 has established itself as the ideal platform for the end-to-end AI pipeline for autonomous machines. It is the fastest, most power-efficient embedded AI computing device available today.
Upon successfully completing the enrollment process for Term 2 of the program, and paying the full tuition fee, eligible enrolled students will receive an education discount on the NVIDIA Jetson TX2 Developer Kit.
Learn with the Best
Sebastian Thrun
Udacity, President
As the founder and president of Udacity, Sebastian’s mission is to democratize education. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass and more.
Dana Sheahen
Udacity, Curriculum Lead
Dana is an electrical engineer with a Masters in Computer Science from Georgia Tech. Her work experience includes software development for embedded systems in the Automotive Group at Motorola, where she was awarded a patent for an onboard operating system.
Ryan Keenan
Udacity, Curriculum Lead
Ryan has a PhD in Astrophysics from the University of Wisconsin-Madison. He is also a lead instructor for the Self-Driving Car and Flying Car Nanodegree programs.
Anthony Navarro
Udacity, Product Lead
Anthony is a US Army combat veteran with an M.S. in Computer Engineering from Colorado State University. Prior to being a Product Lead at Udacity, he was a Senior Software Engineer at Lockheed Martin in their Autonomous Systems R&D division.
Julia Chernushevich
Udacity, Instructor
Julia is an instructor of Mechatronics Engineering at the University of Waterloo. Her previous work experiences include designing electric vehicles for underground mines and leading a prestigious STEM enrichment program for gifted high-school students.
Karim Chamaa
Udacity, Instructor
Karim started his early career as a Mechanical Engineer. He earned his M.S. in Mechatronics and Robotics Engineering from NYU. His specialties include Kinematics, Control, and Electronics.
NVIDIA
NVIDIA Team
NVIDIA is a company built upon great minds and groundbreaking research. GPU deep learning has ignited modern AI - the next era of computing - with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world.
Electric Movement
Electric Movement Team
Electric Movement is a robotics engineering firm that brings to our program invaluable insights about real-world robotics applications, and deep, market-vetted expertise in ROS, robotics, automation, embedded systems, and agile development.
What You Will Learn
Download SyllabusRobotics Software Engineer Syllabus
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Term 1
ROS Essentials, Perception, and Control
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Prerequisite Knowledge
Knowledge of foundational calculus, linear algebra, probability, statistics, basic physics, and intermediate Python is required.
Existing experience with intermediate C++ and knowledge of machine learning techniques is recommended but not required.
See detailed requirements.
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Introduction To Robotics
In this course, you'll get an introduction to your Nanodegree program, and explore the three essential elements of robotics: perception, decision making, and actuation. You'll also build your first project, which is modeled after the NASA Mars Rover Challenge.
Search and Sample Return
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ROS Essentials
ROS provides a flexible and unified software environment for developing robots in a modular and reusable manner. In this course, you'll learn how to manage existing ROS packages within a project, and how to write ROS Nodes of your own in Python.
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Kinematics
Movement is one of the most exciting elements of building a Robot that interacts with the physical world. In this course, you'll learn to control a robotic arm with six degrees of freedom to perform pick and place actions using Inverse Kinematics.
Robotic Arm: Pick and Place
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Perception
Robots perceive the world around them by using sensors. Working with sensor data for perception is a core element of robotics. Here you'll work with 3D point cloud data to perform segmentation tasks using techniques like RANSAC and clustering.
3D Perception
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Controls
Control systems are a central component to most robots. In this course, you’ll learn how a mechanical system can be described in terms of the equations that govern it. You'll then learn how to manage the behavior of the system using a controller. Lastly, you’ll have an opportunity to observe your controller in simulation.
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Deep Learning
Many recent developments in robotics can be attributed to advances in Deep Learning. In this course, you’ll learn about Convolutional Neural Networks (CNN), Fully Convolutional Networks (FCN), and Semantic Segmentation. You will then integrate a Deep Neural Network with a simulated quadcopter drone.
Follow Me
Term 2
Localization, Mapping, and Navigation
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Prerequisite Knowledge
Knowledge of foundational calculus, linear algebra, probability, statistics, basic physics and intermediate Python is required.
Existing experience with intermediate C++ and knowledge of machine learning techniques is recommended but not required.
See detailed requirements.
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Introduction to Term 2
In this course, you’ll get an introduction to Term 2, and explore hardware commonly used in robotics. You’ll learn the uses of common sensors, and which ROS packages you need to support them. Those students with NVIDIA Jetson TX2 Developer Kit hardware will learn how to set up the system and interact with external hardware.
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Robotic Systems Deployment
In this course, you’ll learn new tools, and the embedded workflow as you move from code on a host system to code on a target system. You’ll work through a familiar problem with these new tools, then extend what you’ve learned in a project.
Robotic Inference
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Localization
Learn how Gaussian filters can be used to estimate noisy sensor readings, and how to estimate a robot’s pose relative to a known map of the environment with Monte Carlo Localization (MCL).
Where am I?
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SLAM
Learn how to create a Simultaneous Localization and Mapping (SLAM) implementation with ROS packages and C++. You’ll achieve this by combining mapping algorithms with what you learned in the localization lessons.
Map My World Robot
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Reinforcement Learning for Robotics
Begin by learning how to build a basic end-to-end reinforcement learning agent, termed a deep Q-network (DQN). Then, enhance it to create a more complex agent that can pick and place from visual input.
Pick and Place with Deep Learning
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Path Planning and Navigation
Extend your RL Engine with more advanced techniques for navigation, and compare these with classic approaches. Finally, combine SLAM and navigation into a single comprehensive project!
Home Service Robot
“The NVIDIA Deep Learning Institute and Udacity share a common vision—to provide students with hands-on training and challenging curriculum to accelerate their careers. We’re working with Udacity to build a world-class AI and deep learning program so that students can go on to become leading developers, researchers and academics in a variety of fields.”
— Greg Estes, Vice President of Developer Programs, NVIDIA
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Term 1
Robotics Software
total
Using ROS, learn to solve robotics problems around perception, control, and deep learning.
Term 2
Advanced Robotics Software
total
Learn to apply SLAM and reinforcement learning techniques for solving robotics problems.
Related Nanodegree Programs
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Robotic Software Engineer
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Previous
FAQ
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Why should I enroll in the Robotics Software Engineer Nanodegree program?
The field of robotics is growing at an incredible rate, and demand for software engineers with the right skills far exceeds the current supply. This makes this an ideal time to enter this field, and this groundbreaking program represents a unique opportunity to develop these in-demand skills.
Expert instructors, personalized project reviews, and exclusive hiring opportunities are hallmarks of this program, and in collaboration with the NVIDIA Deep Learning Institute—one of the most exciting and innovative companies in the world—we have built an unrivalled curriculum that offers the most cutting-edge learning experience currently available.
You will graduate from this program having completed several hands-on robotics projects in simulation that will serve as portfolio pieces demonstrating the skills you've acquired. This will enable you to pursue a rewarding career in the robotics field.
Over the course of the program, you'll also have the opportunity to learn about robotics hardware such as the NVIDIA Jetson TX2 Developer Kit—eligible students will even have access to a special education discount on the Jetson TX2 through our collaboration with NVIDIA.
For anyone seeking to launch or advance a career as a Robotics Software Engineer, and who wishes to be a part of the incredible world of robotics, this is the ideal program.
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Project
Search and Sample Return
In this project, you will write code to autonomously map a simulated environment and search for samples of interest.
Project
Robotic Arm: Pick and Place
In this project, you’ll use knowledge of Kinematics and ROS to manipulate a robotic arm in simulation with six degrees of freedom to pick up an object from one location and place it in another without running into obstacles.
Project
3D Perception
In this project, you will complete a tabletop pick and place operation using PR2 in simulation. The PR2 is a common hardware and software platform for robot researchers. This one has been outfitted with a noisy RGB-D sensor that your robot must use to identify and acquire objects from a cluttered space.
Project
Follow Me
In this project, you will train a deep neural network to identify and track a target in simulation and then issue commands to a drone to follow that target. So-called “follow me” applications like this are key to many industries and the techniques you apply here can be extended to other powerful scenarios like adaptive cruise control in autonomous vehicles or human-robot collaboration.
Project
Robotic Inference
Design your own robotic system using inference. You will create a project idea, collect your own data set for classification, and justify network design choices based on your analysis of accuracy and speed on the target system.
Project
Where am I?
You will use the Monte Carlo Localization algorithm in ROS in conjunction with sensor data and a map of the world to estimate a mobile robot’s position and orientation so that your robot can answer the question “Where am I?”
Project
Home Service Robot
You've already used probabilistic methods with SLAM to map and localize a robot. You've also designed deep RL engines to solve end-to-end sense-to-action problems, which can now be applied to navigation. In this project, you'll combine both AI paradigms to simulate a home service robot that can map, localize, and navigate to perform household tasks, moving from one room to another autonomously.
Project
Pick and Place with Deep Learning
Build an RL agent to pick, grip, stack, and pack, using a manipulator arm.
Project
Map My World Robot
Simultaneous Localization and Mapping (SLAM) can be implemented a number of ways in robotics depending on the sensors used via various ROS packages that exist. Here, you will use a ROS SLAM package and simulated sensor data to create an agent that can both map the world around it and localize within it.
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