– Part 01-Module 01-Lesson 01_Welcome

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

– Part 01-Module 01-Lesson 02_What is a Robot

– Part 01-Module 01-Lesson 03_Search and Sample Return

– Part 01-Module 01-Lesson 04_Career Support Overview

– Part 01-Module 01-Lesson 05_Get Help from Peers and Mentors

– Part 01-Module 04-Lesson 01_ Explores – Biologically Inspired Robots

– Part 01-Module 04-Lesson 02_6 Questions on Robotics Careers

– Part 01-Module 05-Lesson 01_Intro to Kinematics

– Part 01-Module 05-Lesson 02_Forward and Inverse Kinematics

– Part 01-Module 06-Lesson 01_ Explores – Human Robot Interaction Robot Ethics

– Part 01-Module 06-Lesson 02_Product Pitch

– Part 01-Module 07-Lesson 01_Perception Overview

– Part 01-Module 07-Lesson 02_Introduction to 3D Perception

– Part 01-Module 07-Lesson 03_Calibration, Filtering, and Segmentation

– Part 01-Module 07-Lesson 04_Clustering for Segmentation

– Part 01-Module 07-Lesson 05_Object Recognition

– Part 01-Module 07-Lesson 06_3D Perception Project

– Part 01-Module 08-Lesson 01_ Explores – Soft Robotics

– Part 01-Module 09-Lesson 01_ Explores – Robot Grasping

– Part 01-Module 10-Lesson 01_Introduction to Controls

– Part 01-Module 10-Lesson 02_Quadrotor Control using PID

– Part 01-Module 11-Lesson 01_ Explores Swarm Robotics

– Part 01-Module 11-Lesson 02_Networking in Robotics

– Part 01-Module 12-Lesson 01_Intro to Neural Networks

– Part 01-Module 12-Lesson 02_TensorFlow for Deep Learning

– Part 01-Module 12-Lesson 03_Deep Neural Networks

– Part 01-Module 12-Lesson 04_Convolutional Neural Networks

– Part 01-Module 12-Lesson 05_Fully Convolutional Networks

– Part 01-Module 12-Lesson 06_Lab Semantic Segmentation

– Part 01-Module 12-Lesson 07_Project Follow Me

– Part 01-Module 12-Lesson 08_Term 1 Outro

– Part 01-Module 13-Lesson 01_Introduction to C++ for Robotics

Discover how ROS provides a flexible and unified software environment for developing robots in a modular and reusable manner. Learn how to manage existing ROS packages within a project, and how to write ROS Nodes of your own in C++.

– Part 02-Module 01-Lesson 01_Introduction to Term 2

– Part 02-Module 01-Lesson 02_The Jetson TX2

Part 02 Module 01 Lesson 03_Interacting with Robotics Hardware

– Part 02-Module 01-Lesson 04_Lab Hardware Hello World

– Part 02-Module 01-Lesson 05_Robotics Sensor Options

– Part 02-Module 02-Lesson 01_Inference Development

– Part 02-Module 02-Lesson 02_Inference Applications in Robotics

– Part 02-Module 02-Lesson 03_Project Robotic Inference

– Part 02-Module 03-Lesson 01_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).

– Part 02-Module 03-Lesson 02_Kalman Filters

– Part 02-Module 03-Lesson 03_Lab Kalman Filters

– Part 02-Module 03-Lesson 04_Monte Carlo Localization

– Part 02-Module 03-Lesson 05_Build MCL in C++

– Part 02-Module 03-Lesson 06_Project Where Am I

– Part 02-Module 04-Lesson 01_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.

– Part 02-Module 04-Lesson 02_ Occupancy Grid Mapping

– Part 02-Module 04-Lesson 03_Grid-based FastSLAM

– Part 02-Module 04-Lesson 04_GraphSLAM

– Part 02-Module 04-Lesson 05_Project Map My World Robot

– Part 02-Module 05-Lesson 01_Intro to RL for Robotics

– Part 02-Module 05-Lesson 02_RL Basics

– Part 02-Module 05-Lesson 03_Q-Learning Lab

– Part 02-Module 05-Lesson 04_Deep RL

– Part 02-Module 05-Lesson 05_DQN Lab

– Part 02-Module 05-Lesson 06_Deep RL Manipulator

– Part 02-Module 05-Lesson 07_Project Deep RL Arm Manipulation

– Part 02-Module 06-Lesson 01_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!

– Part 02-Module 06-Lesson 02_Classic Path Planning

– Part 02-Module 06-Lesson 03_Lab Path Planning

– Part 02-Module 06-Lesson 04_Sample-Based and Probabilistic Path Planning

– Part 02-Module 06-Lesson 05_Research in Navigation

– Part 02-Module 06-Lesson 06_Project Home Service Robot

– Part 02-Module 07-Lesson 01_Strengthen Your Online Presence Using LinkedIn

– Part 01-Module 03-Lesson 01_GitHub

– Part 02-Module 07-Lesson 02_Optimize Your GitHub Profile

– Part 02-Module 08-Lesson 01_Completing the Program

– Part 03-Module 01-Lesson 01_Project Introduction

– Part 03-Module 01-Lesson 02_Project Details

– Part 04-Module 01-Lesson 01_Autonomous Systems Interview Practice

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 Udacity 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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