Bootcamp Robotics Software Engineer Specialization
Bootcamp AI
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About This Course
3 months program
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.
Certificate
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Learning Objectives
The Robotics Software Engineer Nanodegree program is comprised of content and curriculum to support five (5) projects. We estimate that students can complete the program in four (4) months, working 10 hours per week.
Requirements
- 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
Curriculum
841 Lessons
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
01. What is a Robot00:5:19Preview
02. Perception0:54
03. Decision Making0:56
04. Action!0:45
05. Quiz
Search and Sample Return
01. Overview of the Project1:19Preview
02. Explore the Simulator1:50
03. Telemetry and Record Data1:16
04. Jupyter Notebook3:16
05. Workspace Playground
06. The Perception Step
07. Perspective Transform3:40
08. Warp, Threshold, Map
09. Map to World Coordinates
10. Decision Where to Go
11. Test Your Methods
12. Online Rover Lab
13. Environment Setup (Local)
14. More Decisions
15. Take Action!
16. Autonomous Mode
17. Requirements Challenges
18. Common Questions
Project Description – Search and Sample Return42:25
Assignments
Career Support Overview
01. Careers in Robotics1:33
02. Design Review1:40
Get Help from Peers and Mentors
Explores – Biologically Inspired Robots
01. Welcome00:1:26
02. Meet Arpan Chakraborty
03. Actuation12:38
04. Vision9:18
05. Control00:9:33
06. READ Research Papers
07. Biologically Inspired Approaches
08. Robotic Insect
09. Cheetah Locomotion
10. OpenRatSLAM
11. WATCH Concepts in Action
12. DO Lab
13. Lab Configuration
14. Lab Challenge
Questions on Robotics Careers
01. 6 Questions on Robotics Careers
02. Setting Goals
Intro to Kinematics
01. Overview1:14
02. Intro to Kinematics2:36
03. Degrees of Freedom
04. Two DoF Arm
05. Generalized Coordinates
06. Rigid Bodies in Free Space
07. Joint Types
08. Principal Types of Serial Manipulators2:02
09. Serial Manipulator Applications
Forward and Inverse Kinematics
01. Setting up the Problem0:53
02. Coordinate Frames and Vectors2:47
03. Rotation Matrices in 2D
04. Rotation Matrices in 3D4:30
05. Quiz Build a Rotation Matrix
05. Quiz: Build a Rotation Matrix
05.2 Quiz Build a Rotation Matrix
05.2 Quiz Build a Rotation Matrix
05.3 Quiz Build a Rotation Matrix
05.3 Quiz Build a Rotation Matrix
06. Rotations in Sympy
07. Composition of Rotations00:00:00
08. Euler Angles from a Rotation Matrix
09. Translations
10. Homogeneous Transforms and their Inverse
11. Composition of Homogeneous Transforms
12. Denavit-Hartenberg Parameters
13. DH Parameter Assignment Algorithm
14. DH Steps 1-400:00:00
15. DH Steps 5-800:00:00
16. DH Parameter Table00:00:00
17. Forward Kinematics
18. Inverse Kinematics
20. Wrap Up00:00:00
Project: Robotic Arm: Pick & Place
01. Overview1:18
02. Setting up your environment
03. Introduction to Project Tools
04. Gazebo Basics
05. Exploring Gazebo User Interface00:00:00
06. Understanding Unified Robot Description Format (URDF)
07. RViz Basics
08. Moveit! Basics
09. Pick and Place Demo Walkthrough00:00:00
10. KR210 Forward Kinematics 100:00:00
11. KR210 Forward Kinematics 200:00:00
12. KR210 Forward Kinematics 300:00:00
13. Forward Kinematics with Kuka KR210
14. Debugging Forward Kinematics
15. Inverse Kinematics with Kuka KR210
16. Creating IK Solver for Kuka KR210
17. Debugging Inverse Kinematics and Optimization
18. Requirements and Challenge00:00:00
19. Common Questions
20. Project Guide00:00:00
20.2 Project Guide00:00:00
20.3 Project Guide00:00:00
Project Description – Robotic Arm: Pick & Place00:14:41
Project Rubric – Robotic Arm: Pick & Place
Explores – Human Robot Interaction Robot Ethics
01. Introduction00:00:00
02. READ Research Papers
03. Uncanny Valley
04. Designing Robot Heads
05. Human Trajectory Prediction in Crowded Spaces
06. Robot Ethics
07. Robot Ethics from Philosophy of Science
08. WATCH Concepts in Action
09. Meet Andra Keay
10. Exclusive Silicon Valley Robotics00:00:00
11. Meet Cory Kidd
12. Exclusive Catalia Health00:00:00
13. Meet Kaijen Hsiao
14. Exclusive Mayfield Robotics00:00:00
15. DO Lab – Build a Robot Prototype Under $25
Product Pitch
01. Pitching Your Robot Prototype
02. Anatomy of a Product Pitch
03. Showcasing Your Work
Perception Overview
01. Perception and Robotics1:47
02. The Upcoming Project00:00:00
03. Classic vs Modern Approach
04. Summary00:00:00
Introduction to 3D Perception
01. 3D Perception00:00:00
02. Active Sensors
03. Passive Sensors
04. RGB D Cameras
05. Sensor Quiz
06. Why RGB D00:00:00
07. RGB Image Formation00:00:00
08. Adding Depth
09. What is a Point Cloud
10. Point Cloud Types
11. Creating a Point Cloud from RGB D Data
12. 3D Perception Summary00:00:00
Calibration, Filtering, and Segmentation
01. Intro to Calibration, Filtering, and Segmentation00:00:00
02. Sensor Calibration00:00:00
03. RGB Camera Model00:00:00
04. Calibration Pattern
05. OpenCV Calibration
06. Extrinsic Calibration
07. RGBD Calibration in ROS
08. Point Cloud Filtering00:1:20
09. Tabletop Segmentation Exercise
10. Voxel Grid Downsampling
10. Quiz
11. Pass Through Filtering
11. Quiz
12. Segmentation in Perception00:00:00
13. RANSAC Overview
14. RANSAC Plane Fitting
14. Quiz
15. Extracting Indices
16. Outlier Removal Filter
17. Summary00:00:00
Clustering for Segmentation
01. Object Segmentation00:00:50
02. Downside of Model Fitting00:00:00
03. Clustering00:00:00
04. K means Clustering00:00:00
05. K means Playground
06. K means quizzes
06. K means quizzes
07. DBSCAN Algorithm
07. DBSCAN Algorithm (Practice)
08. Comparing DBSCAN and k-means Clustering
09. Clustering with PCL
10. Publish Your Point Cloud
11. Filtering and RANSAC
12. Clustering Objects
13. Cluster Visualization
14. Segmentation Summary00:00:00
Object Recognition
01. Intro to Object Recognition00:00:00
02. Features00:00:00
03. Feature Intuition
03. Feature Intuition
04. Color Spaces00:00:00
05. HSV Intuitions
05. HSV Intuitions
06. Color Histograms00:00:00
07. Histogram in Action
07. Histogram in Action (Practice)
08. Surface Normals00:00:00
09. Normals Intuition
09. Normals Intuition
10. Support Vector Machine
10. Support Vector Machine (Practice)
11. SVM Intuitions
11. SVM Intuitions
12. SVM Image Classification (Practice)
13. Recognition Exercise
14. Generate Features
15. Train Your SVM
15. Train Your SVM
16. Improve Your Model
16. Improve Your Model
17. Object Recognition
18. Recognition Wrap up00:00:00
19. Common Questions
3D Perception Project
01. Project Intro00:00:00
02. Amazon Robotics Challenge
03. Environment Setup
04. Project Demo
05. Perception Pipeline
06. Output yaml files
07. Requirements and Challenge
08. PR2 Collision Avoidance
08. PR2 Collision Avoidance
09. Robot Motion
10. Common Questions
11. Project Summary0:54
Project Rubric – 3D Perception
Project Description – 3D Perception
Explores – Soft Robotics
01. Introduction0:49
03. READ Research Papers
04. Soft Robotics A Perspective
05. Untethered Soft Robot
06. Design and Fabrication
07. Combining Hard and Soft Robots
08. Self Folding Robots
09. Oribotics
10. WATCH Concepts in Action
11. Meet Josh Lessing
12. Exclusive Getting Started in Robotics3:39
13. Exclusive What is Soft Robotics5:53
14. Exclusive What Does Soft Robots Inc. Do00:00:00
15. Exclusive Our Process00:00:00
16. Exclusive Soft Robots and Food00:00:00
17. Exclusive The Future of Soft Robots00:00:00
18. Exclusive Getting Started in Soft Robotics00:00:00
19. Meet Rajat Mishra
20. Exclusive Rajat Mishra00:00:00
21. DO Lab Make an Earthworm00:00:00
22. DO Lab Metamaterials00:00:00
Explores – Robot Grasping
01. Introduction00:1:00
02. READ Research Papers
03. An Overview of Grasping
04. Jamming based Gripper
05. Dex Net 2.0
06. Learning Hand Eye Grasping
07. Slip Detection and Correction
08. WATCH Concepts in Action
09. Meet Josh Lessing
10. Exclusive What is Soft Robotics00:00:00
11. Exclusive Our Process00:00:00
12. Exclusive Soft Robots and Food00:00:00
13. DO Challenge Build a Gripper00:00:00
14. DO Lab Dex Net 2.0 Code
Introduction to Controls
01. Overview of Controls Engineering1:42
02. Open Loop Control1:44
03. Closed Loop Control4:14
04. PID Overview2:39
05. Getting to Know Your Simulator
06. Proportional Control 1
07. Proportional Control 2
08. Proportional Control 3
08. Proportional Control 3
09. Building a P Controller
10. PI Control
11. Building a PI Controller
12. PD Control
13. Building a PD Controller
14. Building a PID Controller
15. PID Control Summary00:00:00
16. Limitations of PID00:00:00
17. Beyond the Ideal Case Integrator Windup
18. Beyond the Ideal Case Noise00:00:00
19. Control Design Objective and Criteria
20. Tuning Strategies 1
21. Tuning Strategies 2
22. Putting It All Together
23. Wrap Up00:00:00
Quadrotor Control using PID
01. Introduction to a Positional Controller0:54
02. Quadrotor Kinematic and Dynamic Model 1
03. Quadrotor Quiz
04. Quadrotor Kinematic and Dynamic Model 2
05. Cascade PID Control
06. Lab Walkthrough
07. Environment Setup
08. Exploring the Sim
09. Helpful Tools
10. Completing PID Controller
11. Hover Controller
12. dynamic_reconfigure
13. Attitude Controller
14. Positional Controller
15. Lab Summary
16. PID Wrap Up0:49
Explores Swarm Robotics
01. Introduction1:14
02. READ Research Papers
03. Swarm Robots in Medicine
04. Kilobots
05. Search and Rescue
06. Self Assembly Swarm Robots
07. WATCH Concepts in Action
08. Meet Sabine Hauert
09. Exclusive Sabine Hauert41:05
10. DO Lab Create a Swarm
Networking in Robotics
01. Skills Recap
02. Companies to Know
03. Networking Next Steps
Intro to Neural Networks
01. Introducing1:56
02. Intro to Neural Networks00:1:53
03. Classification Problems 100:00:00
04. Classification Problems 200:00:00
05. Linear Boundaries00:00:00
06. Perceptrons00:00:00
06. Perceptrons
07. Why Neural Networks00:00:00
08. Perceptrons as Logical Operators00:00:00
08.2 Perceptrons as Logical Operators
08.2 Perceptrons as Logical Operators
08.3 Perceptrons as Logical Operators00:00:00
09. Perceptron Trick00:00:00
09. Perceptron Trick
09.2 Perceptron Trick00:00:00
09.3 Perceptron Trick00:00:00
10. Perceptron Algorithm00:00:00
11. Higher Dimensions00:00:00
11. Higher Dimensions
12. Error Functions00:00:00
13. Log loss Error Function00:00:00
13. Log-loss Error Function
14. Discrete vs Continuous00:00:00
14.2 Discrete vs Continuous00:00:00
14.2 Discrete vs Continuous
15. Softmax00:00:00
15.2 Softmax V23:28
15.2 Question
15.3 Softmax00:00:00
16. One Hot Encoding1:20
17. Maximum Likelihood00:00:00
17.2 Maximum Likelihood00:00:00
17.2 Maximum Likelihood
18. Maximizing Probabilities00:00:00
18.1 Quiz Cross Entropy00:00:00
18.2 Maximizing Probabilities
19. Cross Entropy 100:00:00
20. Cross Entropy 200:00:22
20.1 CrossEntropy Formula00:00:00
21. Multi Class Cross Entropy00:00:00
21. Multi-Class Cross Entropy
22. Logistic Regression00:00:00
22.1 Logistic Regression-Minimizing The Error Function00:00:00
23. Gradient Descent00:00:00
24. Logistic Regression Algorithm
25. Non Linear Regions00:00:00
26. Non Linear Models00:00:00
27. Neural Network Architecture00:00:00
27.2 Neural Network Architecture00:00:00
27.2 Quiz
27.3 Multiple layers00:00:00
27.3 Neural Network Architecture00:00:00
27.4 Multi-Class Classification00:00:00
27.4 Neural Network Architecture00:00:00
Question
28. Feedforward00:00:00
28.2 Error Function00:00:00
29. Backpropagation00:00:00
29.2 Backpropagation V200:00:00
29.3 Chain Rule00:00:00
29.4 Calculating The Gradient00:00:00
30. Further Reading
31. Neural Networks Wrap Up00:00:00
TensorFlow for Deep Learning
01. Introducing0:44
02. What is Deep Learning00:00:53
03. Solving Problems Big and Small1:29
04. Let’s Get Started!00:00:49
05. Installing Tensorflow
06. Hello, Tensor World!
07. Transition to Classification
08. Classification0:48
09. Let’s make a deal00:00:00
10. Training Your Logistic Classifier00:00:00
11. Tensorflow Softmax
11. Tensorflow Softmax
12. TensorFlow Cross Entropy00:00:00
13. Minimizing Cross Entropy00:00:00
14. Practical Aspects of Learning00:00:00
15. Quiz Numerical Stability00:00:00
16. Normalized Inputs and Initial Weights00:00:00
17. Measuring Performance00:00:00
18. Validation and Test Set Size00:00:00
19. Quiz Validation Set Size00:00:00
19. Quiz: Validation Set Size
20. Validation Set Size Continued00:00:00
21. Optimizing a Logistic Classifier00:00:00
22. Stochastic Gradient Descent00:00:00
23. Momentum and Learning Rate Decay00:00:00
24. Parameter Hyperspace!00:00:00
25. Quiz Mini batch
26. Epochs
27. Lab TensorFlow Neural Network
28. Understanding Jupyter Workspaces
29. Jupyter Workspace TensorFlow
Deep Neural Networks
01. Intro to Deep Neural Networks00:00:00
02. Quiz Number of Parameters0:16
02. Quiz: Number of Parameters
03. Linear Models are Limited00:00:00
04. Quiz Rectified Linear Units00:00:00
05. Network of ReLUs00:00:00
06. 2 Layer Neural Network
07. Quiz TensorFlow ReLUs
08. No Neurons00:00:00
09. The Chain Rule00:00:00
10. Backprop00:00:00
11. Deep Neural Network in TensorFlow
12. Training a Deep Learning Network00:00:00
13. Save and Restore TensorFlow Models
14. Finetuning
15. Regularization Intro00:00:00
16. Regularization00:00:00
17. Regularization Quiz00:00:00
18. Dropout00:00:00
19. Dropout Pt. 200:00:00
20. Quiz TensorFlow Dropout
21. Lab Tensorflow Deep Neural Network
22. Jupyter Workspace DNN
Convolutional Neural Networks
01. Intro To CNNs0:11
02. Color00:00:00
03. Statistical Invariance00:00:00
04. Convolutional Networks00:00:00
05. Intuition
06. Filters
07. Feature Map Sizes00:00:00
07. Feature Map Sizes 200:00:00
08. Convolutions continued00:00:00
09. Parameters
10. Quiz Convolution Output Shape
10. Quiz: Convolution Output Shape
11. Solution Convolution Output Shape
12. Quiz Number of Parameters
12. Quiz Number of Parameters
13. Solution Number of Parameters
14. Quiz Parameter Sharing
14. Quiz Parameter Sharing
15. Solution Parameter Sharing
16. Visualizing CNNs
17. TensorFlow Convolution Layer
18. Explore The Design Space00:00:00
19. TensorFlow Max Pooling
20. Pooling Intuition
21. Quiz Pooling Mechanics
21. Quiz: Pooling Mechanics
22. Solution Pooling Mechanics
23. Pooling Practice
25. 1×1 Convolutions00:00:00
26. Inception Module00:00:00
27. Convolutional Network in TensorFlow
28. TensorFlow Convolution Layer
29. Solution TensorFlow Convolution Layer
30. TensorFlow Pooling Layer
31. Solution TensorFlow Pooling Layer
32. Lab Tensorflow CNN
33. GPU Workspace Introduction
35. CNNs Additional Resources
36. CNNs Wrapup00:00:00
Fully Convolutional Networks
01. Intro00:00:00
02. Why Fully Convolutional Networks (FCNs)00:00:00
03. Fully Convolutional Networks00:00:00
04. Fully Connected to 1×1 Convolution00:00:00
04. Fully Connected to 1×1 Convolution
05. 1×1 Convolution Quiz
06. 1×1 Convolution Quiz Solution
07. Transposed Convolutions00:00:00
08. Transposed Convolution Quiz
09. Transposed Convolution Quiz Solution
10. Skip Connections00:00:00
11. FCNs In The Wild00:00:00
12. Outro00:00:00
Lab Semantic Segmentation
01. Intro00:00:00
02. Bounding Boxes00:00:00
03. Semantic Segmentation00:00:00
04. Scene Understanding00:00:00
05. Lab Semantic Segmentation
06. Lab Getting Started
07. Lab Keras00:00:00
08. Lab Encoder
09. Lab Batch Normalization
10. Lab Decoder
10. Lab: Decoder
11. Lab Layer Concatenation
12. IoU00:00:00
13. IoU Quiz
13. IoU Quiz
14. Outro00:00:00
15. Jupyter Workspace Semantic Segmentation
Project Follow Me
01. Project Intro00:00:00
02. Setting up your Local Environment
03. Working with the Simulator
04. Collecting your Data
05. Data Collection Guide1:18
06. Building your Segmentation Network
07. Training and Validation
08. GPU Workspace Introduction
09. Workspace Best Practices
10. Workspace Follow Me
11. AWS GPU Instance Preparation
12. AWS GPU Instance Set Up
13. Testing your Model in the Simulator
Project Rubric - Follow Me
Project Description - Follow Me
Term 1 Outro
01. Outro00:00:00
02. Autonomous Systems Interview
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++.
01. Introducing Karim0:44
02. Welcome!1:18
03. Overview1:08
04. Transitioning1:18
05. Ubuntu Setup00:00:00
06. Editor Choice00:00:00
06. Editor Choice
07. Hello, World!00:00:00
08. Compile and Execute00:00:00
08. Compile and Execute
09. Functions and Data Structures00:00:00
09. Functions and Data Structures
10. Classes and Objects00:00:00
10. Classes and Objects
11. Inheritance and Pointers00:00:00
11. Inheritance and Pointers
12. Template Classes00:00:00
13. External Libraries00:00:00
13. External Libraries
14. ROS Nodes3:26
15. Rover Control00:00:00
15. Rover Control
16. Challenge!0:53
Introduction to Term 2
01. Term 2 Handoff0:55
02. Introduction1:20
03. Objectives00:2:39
04. Outline1:50
05. Class Schedule
The Jetson TX2
01. Introduction1:52
02. Jetson Overview2:45
03. Jetson Developer Kit Board
03. Jetson Developer Kit Board
04. Jetson Setup1:27
05. Host Setup
06. JetPack
07. SSH from Host
08. Recap0:19
Interacting with Robotics Hardware
01. Overview00:00:52
02. Battery00:2:20
02. Battery
03. GPIO00:2:33
03. GPIO
04. Breadboard1:55
05. Motors and Sensors
05. Motors and Sensors
06. Recap0:28
Lab Hardware Hello World
01. Overview0:49
02. Hardware List
03. LED Circuit4:06
04. Equivalent Resistance
04. Equivalent Resistance
05. Connect the LED Circuit
06. Transistor Switch Circuit00:00:00
06. Transistor Switch Circuit
07. Connect the Transistor Circuit
08. Software Control
09. Recap0:21
Robotics Sensor Options
01. Overview0:30
02. Cameras0:36
03. IMU00:00:37
04. RADAR00:00:00
05. LIDAR00:00:00
06. IR00:00:00
07. Variable Resistors00:00:00
08. Ultrasonic00:00:00
09. Encoders00:00:00
10. Recap00:00:00
Inference Development
01. Overview1:34
02. DIGITS MINST Example4:02
03. Lab Nvidia DIGITS
04. Inference on the Jetson0:32
05. Deploy the Model0:27
06. Lab Nvidia Deployment
07. Recap0:48
Inference Applications in Robotics
01. Overview1:04
02. Embedded Examples00:00:00
03. Metrics
03. Metrics
04. SpeedAccuracy Tradeoff
05. Tradeoff Quiz
05. Tradeoff Quiz
06. Recap00:00:00
Project Robotic Inference
01. Overview00:00:00
02. Practicing the DIGITS Workflow
03. Picking your Robotic Inference Idea1:05
04. Collecting your own Data
05. Importance of Documentation
06. Optional Deploying your Inference Project
07. DIGITS Workspace
08. Recap00:00:00
09. Common Questions
Project Rubric Robotic Inference
Project Description 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).
01. Localization in Robotics00:2:02
02. Localization Challenges1:54
02. Localization Challenges
02. Localization Challenges Solution0:22
03. Overview
Kalman Filters
01. Overview0:38
02. What’s a Kalman Filter00:2:10
03. History1:11
04. Applications0:45
05. Variations00:00:51
06. Robot Uncertainty3:22
07. Kalman Filter Advantage1:27
08. 1D Gaussian
08.2 1D Gaussian
08.2 1D Gaussian
08.3 1D Gaussian
08.3 1D Gaussian
09. Designing 1D Kalman Filters00:2:20
10. Measurement Update00:00:48
10. Measurement Update – Quiz
10. Measurement Update – Quiz
10. Measurement Update 1D00:00:19
10. Measurement Update 1D
10.3 Measurement Update
11. State Prediction00:1:26
12. 1D Kalman Filter1:54
13. Multivariate Gaussian
13.2 Multivariate Gaussian00:00:00
14. Intro to Multidimensional KF3:37
15. Design of Multidimensional KF
15.2 Design of Multidimensional KF00:00:00
16. Introduction to EKF00:00:00
17. EKF
18. EKF Example
19. Recap00:00:00
Lab Kalman Filters
01. Introduction1:24
02. Sensor Fusion2:10
03. Catkin Workspace
04. Workspace
05. TurtleBot Gazebo Package00:00:00
06. Robot Pose EKF Package00:00:00
07. Odometry to Trajectory Package00:00:00
08. TurtleBot Teleop Package00:00:00
09. Rviz Package00:00:00
10. Main Launch00:00:00
11. Rqt Multiplot00:00:00
12. Outro00:00:00
Monte Carlo Localization
01. Introduction00:00:00
02. What’s MCL00:00:00
03. Power of MCL00:00:00
04. Particle Filters00:00:00
05. Bayes Filtering
06. MCL The Algorithm00:00:00
06. MCL: The Algorithm
07. MCL in Action00:00:00
07. MCL in Action
08. Outro00:00:00
Build MCL in C++
01. Introduction1:28
02. Robot Class00:00:00
03. First Interaction00:00:00
04. Motion and Sensing
05. Noise00:00:00
06. Particle Filter00:00:00
07. Importance Weight00:00:00
08. Resampling00:00:00
09. Resampling Wheel00:00:00
10. Error00:00:00
11. Graphing00:00:00
12. Workspace
13. Images00:00:00
14. Outro00:00:00
GraphSLAM
01. Introduction00:00:00
02. Graphs00:00:00
03. Constraints00:00:00
04. Front End vs Back End00:00:00
05. Maximum Likelihood Estimation
06. MLE Example
06. MLE Example
07. Numerical Solution to MLE
08. Mid Lesson Overview00:00:00
09. 1 D to n D
10. Information Matrix and Vector00:00:00
11. Inference
12. Nonlinear Constraints
13. Graph SLAM at a Glance00:00:00
14. Intro to 3D SLAM With RTAB Map00:00:00
15. 3D SLAM With RTAB Map00:00:00
16. Visual Bag of Words00:00:00
17. RTAB Map Memory Management00:00:00
18. RTAB Map Optimization and Output
19. Outro00:00:00
Project Where Am I
01. Overview
02. Gazebo Hello, world!
02. Gazebo: Hello, world!
03. Robot Model Basic Setup
03. Robot Model: Basic Setup
04. Let There Be Sight!
04. Let There Be Sight!
05. RViz Integration
06. Localization Map
07. Localization AMCL Package
08. Localization Parameter Tuning 1
09. Localization Parameter Tuning 2
10. Launching and Testing
11. Outro
12. Common Questions
13. Project Challenge00:00:00
14. Project Workspace
Project Rubric Where Am I
Project Description 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.
01. Introduction00:00:00
02. Mapping00:00:00
03. SLAM00:00:00
Occupancy Grid Mapping
01. Introduction00:00:00
02. Importance of Mapping00:00:00
02. Importance of Mapping
03. Challenges and Difficulties00:00:00
03. Challenges and Difficulties
04. Mapping with Known Poses00:00:00
04. Mapping with Known Poses
05. Posterior Probability
05. Posterior Probability
06. Grid Cells00:00:00
06. Grid Cells
07. Computing the Posterior
07. Computing the Posterior
08. Filtering00:00:00
08. Filtering
09. Binary Bayes Filter Algorithm
10. Occupancy Grid Mapping Algorithm00:00:00
11. Inverse Sensor Model00:00:00
12. Generate the Map
13. Workspace
14. Multi Sensor Fusion
15. Introduction to 3D Mapping
16. 3D Data Representations00:00:00
17. Octomap00:00:00
18. Outro00:00:00
Grid-based FastSLAM
01. Introduction00:00:00
01. Introduction
02. Online SLAM
02. Online SLAM
03. Full SLAM00:00:00
03. Full SLAM
04. Nature of SLAM
04. Nature of SLAM
05. Correspondence00:00:00
05. Correspondence
06. SLAM Challenges
06. SLAM Challenges
07. Particle Filter Approach to SLAM00:00:00
08. Introduction to FastSLAM00:00:00
08. Introduction to FastSLAM
09. FastSLAM Instances
09. FastSLAM Instances
10. Adapting FastSLAM to Grid Maps
10. Adapting FastSLAM to Grid Maps
11. Grid based FastSLAM Techniques00:00:00
11. Grid-based FastSLAM Techniques
12. The Grid based FastSLAM Algorithm00:00:00
12. The Grid-based FastSLAM Algorithm
13. gmapping ROS Package00:00:00
14. Workspace
15. SLAM with ROS00:00:00
Project Map My World Robot
01. Project Introduction
02. Extending your Robot Creation Sensor Upgrade
03. Extending your Robot Creation Launch Files
04. Debugging in ROS Transform Frames
05. Debugging in ROS roswtf
06. Debugging in ROS rqt common plugins
07. RTAB Map Visualization Tools Database Viewer
08. RTAB Map Visualization Tools rtabmapviz
09. Final Considerations
10. Going Above and Beyond Localization
11. Going Above and Beyond Other RTAB Map Features
12. Ubuntu Install and Common Troubleshooting
13. Workspace
14. Common Questions
Project Rubric Map My World Robot
Project Description Map My World Robot
Intro to RL for Robotics
01. Introduction00:00:00
02. Deep RL in Robotics00:00:00
02. Deep RL in Robotics
03. Lesson Roadmap00:00:00
RL Basics
01. Introduction1:32
02. Applications2:16
03. The Setting4:58
04. Reference Guide
05. The Setting, Revisited00:00:00
06. Episodic vs. Continuing Tasks00:00:00
07. Quiz: Test Your Intuition
07. Quiz: Test Your Intuition
08. Quiz: Episodic or Continuing?
08. Quiz: Episodic or Continuing?
09. The Reward Hypothesis00:00:00
10. Goals and Rewards, Part 100:00:00
11. Goals and Rewards, Part 200:00:00
12. Quiz: Goals and Rewards
13. Cumulative Reward00:00:00
14. Discounted Return00:00:00
15. Quiz: Pole-Balancing
16. MDPs, Part 100:00:00
17. MDPs, Part 200:00:00
18. Quiz: One-Step Dynamics, Part 1
18. Quiz: One-Step Dynamics, Part 1
19. Quiz: One-Step Dynamics, Part 2
19. Quiz: One-Step Dynamics, Part 2
20. MDPs, Part 300:00:00
21. Summary
22. Policies00:00:00
23. Quiz: Interpret the Policy
23. Quiz: Interpret the Policy
24. Gridworld Example00:00:00
25. State-Value Functions00:00:00
26. Bellman Equations00:00:00
27. Quiz: State-Value Functions
27. Quiz: State-Value Functions
28. Optimality00:00:00
29. Action-Value Functions00:00:00
30. Quiz: Action-Value Functions
30. Quiz: Action-Value Functions
31. Optimal Policies00:00:00
32. Quiz: Optimal Policies
32. Quiz: Optimal Policies
Q-Learning Lab
01. Introduction00:00:00
02. Q-Learning Algorithm00:00:00
02. Q-Learning Algorithm
03. Q-Learning Example
03. Q-Learning Example
04. OpenAI Gym
04. OpenAI Gym
05. Q-Learning Notebook
06. Recap00:00:00
Deep RL
01. Introduction00:00:00
02. From RL to Deep RL00:00:00
02. From RL to Deep RL
03. DQN Overview00:00:00
04. Deep Q Network00:00:00
05. Experience Replay00:00:00
06. Fixed Q Targets00:00:00
07. Recap00:00:00
DQN Lab
01. Introduction00:00:00
02. PyTorch Intro
02. PyTorch Intro
03. PyTorch Part 1
04. PyTorch Part 2
05. DQN Notebook
06. Recap00:00:00
Deep RL Manipulator
01. Introduction00:00:00
02. C/C++ API
02. C/C++ API
03. Catch Game Sample
03. Catch Game Sample
04. Fruit Game Sample
04. Fruit Game Sample
05. Test the API Workspace
06. Project Environment
06. Project Environment
07. Gazebo Arm Plugin
08. Introduction to LSTMs
09. Recap00:00:00
Project Deep RL Arm Manipulation
01. Overview
02. Getting Started
03. Tasks
04. Recap and Task List
05. Additional Project Challenges
06. Project Workspace
Project Rubric - Deep RL Arm Manipulation
Project Description - Deep RL Arm Manipulation
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!
01. Lesson Overview00:00:00
02. Applications
Classic Path Planning
01. Introduction to Path Planning00:00:00
02. Examples of Path Planning00:00:00
02. Examples of Path Planning
03. Approaches to Path Planning
04. Discrete Planning00:00:00
05. Continuous Representation00:00:00
06. Minkowski Sum
07. Quiz: Minkowski Sum
07. Quiz: Minkowski Sum
08. Minkowski Sum C++
09. Translation and Rotation00:00:00
10. 3D Configuration Space
10. 3D Configuration Space
11. Discretization00:00:00
12. Roadmap
13. Visibility Graph
13. Visibility Graph
14. Voronoi Diagram
14. Voronoi Diagram
15. Cell Decomposition
16. Approximate Cell Decomposition
16. Approximate Cell Decomposition
17. Potential Field
18. Discretization Wrap-Up00:00:00
19. Graph Search00:00:00
20. Terminology
21. Breadth-First Search00:00:00
21. Breadth-First Search
21.2 Breadth-First Search00:00:00
21.2 Breadth-First Search
22. Depth-First Search00:00:00
22. Depth-First Search
22.2 Depth-First Search00:00:00
22.2 Depth-First Search
23. Uniform Cost Search00:00:00
23. Uniform Cost Search
23.2 Uniform Cost Search00:00:00
23.2 Uniform Cost Search
24. A* Search00:00:00
24. A* Search
25. Overall Concerns
26. Graph-Search Wrap-Up00:00:00
27. Discrete Planning Wrap-Up00:00:00
Lab Path Planning
01. Introduction
02. Modeling the Problem
03. BFS: Expansion List
04. BFS: Expansion Vector
05. BFS: Shortest Path
06. A*: Shortest Path
07. Comparison
08. A*: Real-World Map
09. A*: Visualization
Sample-Based and Probabilistic Path Planning
01. Introduction to Sample-Based & Probabilistic Path Planning00:00:00
02. Why Sample-Based Planning
03. Weakening Requirements
04. Sample-Based Planning
05. Probabilistic Roadmap (PRM)00:00:00
05. Probabilistic Roadmap (PRM)
06. Rapidly Exploring Random Tree Method (RRT)00:00:00
06. Rapidly Exploring Random Tree Method (RRT)
07. Path Smoothing00:00:00
08. Overall Concerns
09. Sample-Based Planning Wrap-Up00:00:00
10. Introduction to Probabilistic Path Planning00:00:00
11. Markov Decision Process
12. Policies
12. Policies
13. State Utility
13. State Utility
15. Probabilistic Path Planning Wrap-Up00:00:00
Research in Navigation
01. Introduction
02. Navigation with Deep RL
03. A3C00:00:00
04. Actor-Critic Quiz
04. Actor-Critic Quiz
05. Challenges in RL Navigation
03.2 A3C00:00:00
Project: Home Service Robot
01. Introduction00:00:00
02. Working Environment
03. Workspace
04. Shell Scripts
05. Catkin Workspace
06. Building Editor00:00:00
07. Testing SLAM
08. Wall Follower00:00:00
09. Testing Navigation00:00:00
10. Reaching Multiple Goals00:00:00
11. Modeling Virtual Objects00:00:00
12. Putting it all Together00:00:00
Project Rubric - Home Service Robot
Project Description - Home Service Robot
Project Details
01. Project Specification
02. Getting Started
03. Scoring Criteria
04. Path Planning
05. Project Workspace
06. Submission Instructions
07. Project Walkthrough
08. Hints
09. Maze #1 Leaderboard
10. Maze #2 Leaderboard
11. Contest Maze Leaderboard
Student Feedback
5.0
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Reviews (1)
Carl Owen
12 months ago
Amazing!
Free
