Artificial Intelligence for Robotics

Artificial Intelligence for Robotics

Instructor: Packt - Course Instructors

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11 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

11 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Apply AI and ML techniques to enhance robot perception and decision-making
Implement object recognition and navigation strategies using neural networks and algorithms
Integrate natural language processing to enable voice and personality features in robots

Skills you'll gain

Tools you'll learn

Python Programming

Details to know

Shareable certificate

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Assessments

11 assignments

Taught in English

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There are 11 modules in this course

This course dives deep into the integration of artificial intelligence and machine learning within robotics. You will learn to build intelligent robots capable of performing real-world tasks using ROS 2, Python, OpenCV, and advanced AI/ML techniques. By focusing on neural networks, computer vision, and natural language processing, this course will help you enhance robot functionality for complex tasks.

Throughout the course, you will improve your skills in applying AI and ML techniques, implementing object recognition systems, and navigating robots. The content is designed for hands-on learning, allowing you to work on practical problems while developing a real-world understanding of intelligent robotics systems. What sets this course apart is its unique combination of theory and practical application. The course integrates state-of-the-art AI/ML concepts into the design and development of robotics projects, equipping you with the tools to work on intelligent robot systems. The hands-on approach makes the theoretical knowledge more accessible and applicable in real-life scenarios. This course is perfect for robotics engineers, AI/ML enthusiasts, and students with a background in Python and electronics. If you're looking to create smarter, more capable robots, this course will help you take your skills to the next level.

In this section, we explore integrating AI into robotics, focusing on decision-making, learning, and autonomy. Key concepts include neural networks, reinforcement learning, and autonomous behavior design.

What's included

2 videos9 readings1 assignment

2 videos Total 2 minutes

Course Overview 1 minute
The Foundation of Robotics and Artificial Intelligence - Overview Video 1 minute

9 readings Total 90 minutes

Introduction 10 minutes
Are Recent Developments in AI Anything New 10 minutes
The Basics of Robotics 10 minutes
When Do You Need AI for Your Robot 5 minutes
Introducing the Robot and Our Development Environment 10 minutes
Software Components (ROS, Python, and Linux) 10 minutes
Using Control Loops 10 minutes
Types of Control Loops 5 minutes
Running a Control Loop 20 minutes

1 assignment Total 10 minutes

Exploring the Foundations of Robotics and AI 10 minutes

In this section, we explore robot anatomy, subsumption architecture, and ROS 2 setup to enable practical robotic system development through structured hardware and software configuration.

What's included

1 video5 readings1 assignment

In this section, we explore systems engineering principles for robot design, focusing on use cases, storyboards, and hardware/software requirements to guide practical robotic task development.

What's included

1 video5 readings1 assignment

In this section, we explore using convolutional neural networks (CNNs) and YOLOv8 for object recognition, focusing on image processing, supervised learning, and real-world applications in robotics and AI.

What's included

1 video5 readings1 assignment

1 video Total 1 minute

Recognizing Objects Using Neural Networks and Supervised Learning - Overview Video 1 minute

5 readings Total 70 minutes

Introduction 20 minutes
Artificial Neurons 10 minutes
Using YOLOv8 An Object Recognition Model 10 minutes
Understanding How to Train Our Toy Detector 20 minutes
Building the Toy Detector 10 minutes

1 assignment Total 10 minutes

Fundamentals of Object Recognition with Neural Networks 10 minutes

In this section, we explore training robots using reinforcement learning and genetic algorithms. Key concepts include Q-learning for grasping and GA-based path planning for autonomous manipulation.

What's included

1 video8 readings1 assignment

1 video Total 1 minute

Picking Up and Putting Away Toys Using Reinforcement Learning and Genetic Algorithms - Overview Video 1 minute

8 readings Total 125 minutes

Introduction 10 minutes
Designing the Software 10 minutes
Setting Up the Solution 5 minutes
How Do We Pick Actions 5 minutes
Creating the Interface to the Arm 30 minutes
Introducing Q-Learning for Grasping Objects 30 minutes
Introducing GAs 5 minutes
Building a GA Process 30 minutes

1 assignment Total 10 minutes

Learning from Feedback and Evolution in Robotics 10 minutes

In this section, we explore robot speech recognition using NLP, STT, and TTS, and implement command processing with Mycroft to enhance natural language understanding and response generation.

What's included

1 video7 readings1 assignment

1 video Total 1 minute

Teaching a Robot to Listen - Overview Video 1 minute

7 readings Total 85 minutes

Introduction 10 minutes
Listening for the Wake Word 10 minutes
Clarifying the Intent 10 minutes
Adding Skills 5 minutes
Cleaning up the toys 20 minutes
Telling Jokes 20 minutes
Receiving Jokes 10 minutes

1 assignment Total 10 minutes

Robot Listening and Speech Recognition Fundamentals 10 minutes

In this section, we explore robot navigation strategies without SLAM, focusing on AI-driven obstacle avoidance and sensor-based movement for efficient task execution.

What's included

1 video7 readings1 assignment

1 video Total 1 minute

Teaching the Robot to Navigate and Avoid Stairs - Overview Video 1 minute

7 readings Total 60 minutes

Introduction 10 minutes
Understanding the SLAM Methodology 10 minutes
Exploring Alternative Navigation Techniques 10 minutes
Implementing Neural Networks 5 minutes
Processing the image 5 minutes
Training the Neural Network for Navigation 10 minutes
CNN Robot Control Implementation 10 minutes

1 assignment Total 10 minutes

Understanding Robot Navigation and Sensor Use 10 minutes

In this section, we explore AI decision-making tools like decision trees, path planning, and expert systems for robotics.

What's included

1 video8 readings1 assignment

1 video Total 1 minute

Putting Things Away - Overview Video 1 minute

8 readings Total 105 minutes

Introduction 10 minutes
What Do We Mean by Pruning? 5 minutes
Creating Self-Classifying Decision Trees 30 minutes
Understanding Entropy 20 minutes
Random Forests 10 minutes
Developing a Map Based on Our Knowledge 10 minutes
Introducing the A* Algorithm 10 minutes
Introducing the D (D-star or Dynamic A) Algorithm 10 minutes

1 assignment Total 10 minutes

Navigating Algorithms and Data Structures 10 minutes

In this section, we explore simulating artificial personality in robots using finite state machines and AI. Key concepts include behavior modeling and emotion simulation for practical robotic applications.

What's included

1 video10 readings1 assignment

1 video Total 1 minute

Giving the Robot an Artificial Personality - Overview Video 1 minute

10 readings Total 80 minutes

Introduction 10 minutes
A Brief Introduction to the (Obsolete) Turing Test, Chatbots, and Generative AI 5 minutes
The Art and Science of Simulation 10 minutes
An Emotion State Machine 5 minutes
Playing the Emotion Game 5 minutes
Creating a Model of Human Behavior 5 minutes
Constructing a Personality 10 minutes
Adding Context 10 minutes
Developing the Robot Emotion Engine 10 minutes
Creating Human Information Storage 10 minutes

1 assignment Total 10 minutes

Robot Emotion and Artificial Personality 10 minutes

In this section, we examine when to stop in AI development, explore robotics career paths, and assess AI risks to support informed decision-making in real-world applications.