Robotics Software Engineer Bootcamp

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About This Course


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Learning Objectives

The Robotics Software Engineer Nanodegree program focuses on teaching the core robotics skills needed for a successful robotics software engineering career.
The program focuses on Localization, Mapping, SLAM, Path Planning, and Navigation. These are taught using C++ and the Robot Operating System (ROS) framework.


  • To succeed in this Nanodegree program, you should have experience with the following:
  • Advanced knowledge in any object-oriented programming language, preferably C++
  • Intermediate Probability
  • Intermediate Calculus
  • Intermediate Linear Algebra
  • Basic Linux Command Lines


905 Lessons


Learn how to simulate your first robotic environment with Gazebo, the most common simulation engine used by Roboticists around the world.
Meet Your Instructors2:10Preview
Projects You Will Build1:39Preview
Access the Career Portal
How Do I Find Time for My Nanodegree?00:00:00

What is a Robot

Search and Sample Return

Project - Search and Sample Return

Career Support Overview

Get Help from Peers and Mentors

Explores – Biologically Inspired Robots

Questions on Robotics Careers

Intro to Kinematics

Forward and Inverse Kinematics

Project: Robotic Arm: Pick & Place

Project 1: Build My World

Explores – Human Robot Interaction Robot Ethics

Product Pitch

Perception Overview

Introduction to 3D Perception

Introduction to 3D Perception

Calibration, Filtering, and Segmentation

Clustering for Segmentation

Object Recognition

3D Perception Project

Explores – Soft Robotics

Explores – Robot Grasping

Introduction to Controls

Introduction to Controls

Quadrotor Control using PID

Explores Swarm Robotics

Networking in Robotics

Intro to Neural Networks

Intro to Neural Networks

TensorFlow for Deep Learning

Deep Neural Networks

Convolutional Neural Networks

Fully Convolutional Networks

Lab Semantic Segmentation

Project Follow Me

Term 1 Outro

Introduction to C++ for Robotics

C++ for Robotics

Introduction to Term 2

The Jetson TX2

Interacting with Robotics Hardware

Lab Hardware Hello World

Robotics Sensor Options

Inference Development

Inference Applications in Robotics

Project Robotic Inference

Project: Robotic Inference

Introduction to Localization

Learn how Gaussian filters can be used to estimate noisy sensor readings, and how to estimate a robot’s position relative to a known map of the environment with Monte Carlo Localization (MCL).

Kalman Filters

Lab Kalman Filters

Monte Carlo Localization

Build MCL in C++


Project Where Am I

Project: Where Am I?

Introduction to Mapping and 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.

Occupancy Grid Mapping

Grid-based FastSLAM

Project Map My World Robot

Project: Map My World Robot

Intro to Path Planning and Navigation

Learn different Path Planning and Navigation algorithms. Then, combine SLAM and Navigation into a home service robot that can autonomously transport objects in your home!

Classic Path Planning

Lab Path Planning

Sample-Based and Probabilistic Path Planning

Research in Navigation

Project: Home Service Robot

Project: Home Service Robot

Project Details

Intro to RL for Robotics

RL Basics

Q-Learning Lab

Deep RL


Deep RL Manipulator

Project Deep RL Arm Manipulation

Project: Deep RL Arm Manipulation

Job Search

Find your dream job with continuous learning and constant effort

Refine Your Entry-Level Resume

Craft Your Cover Letter

Optimize Your GitHub Profile

Develop Your Personal Brand

905 lectures

Material Includes

  • Workspaces
  • Hands-on Projects
  • Quizzes
  • Progress Tracker

Pick a plan