Lesson List

Welcome

Learn how to simulate your first robotic environment with Gazebo, the most common simulation engine used by Roboticists around the world.

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

Set up your work environment

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++

GraphSLAM

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

DQN Lab

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

Go to course home
01. What It Takes

To graduate, you need to pass every project.

The videos, text lessons and quizzes are recommended but optional.

We know from survey and behavioral data that graduating depends primarily on your commitment and your persistence.

But at some point, you will get stuck. Doubt can set in.

What you choose to do when this happens is what separates successful online learners from others.

Don’t panic. Don’t quit. Be patient, and work the problem.

Remember that you will encounter many of the same problems as everyone else.

We are here to help, and so are your classmates.

When you are stuck, or looking for encouragement, you’ll find Bootcamp AI mentors and other students pushing you to graduation.

The most important feedback you get from mentors will be directly from your project reviews.

You will also find mentors, classmates and alumni on two platforms to get unblocked fast: Knowledge for searchable, upvoted Q&A, and Student Hub for real time collaboration.

Have questions? Head to Knowledge for discussion with the Bootcamp AI Community.

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