Artificial Vision and Language Processing for Robotics
Artificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You'll compare different methods used to work with robots and explore computer vision, its algorithms, and limits. You'll then learn how to control the robot with natural language processing commands. You'll study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You'll create a simple Word2Vec model with Keras, as well as build a convolutional neural network (CNN) and improve it with data augmentation and transfer learning. You'll study the ROS and build a conversational agent to manage your robot. You'll also integrate your agent with the ROS and convert an image to text and text to speech. You'll learn to build an object recognition system using a video.
By the end of this course, you'll have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment.
After completing this course, you will be able to:
- Explore the ROS and build a basic robotic system
- Identify conversation intents with NLP techniques
- Learn and use the embedding with Word2Vec and GloVe
- Use deep learning to implement artificial intelligence (AI) and object recognition
- Develop a simple object recognition system using CNNs
- Integrate AI with ROS to enable your robot to recognize objects
Artificial Vision and Language Processing for Robotics takes a practical approach to equip robotics developers with tools for creating systems that integrate computer vision and NLP to control a robot. The course is divided into three parts: NLP, computer vision, and robotics. It introduces the advanced topics after a detailed introduction to the basics. It contains multiple activities for you to practice and apply your new skills in a highly relevant context.
For the optimal student experience, we recommend the following hardware configuration:
- Processor: Intel Core i5 or equivalent
- Memory: 4 GB RAM
- Storage: 5 GB available space
- An internet connection
Lesson 1: Fundamentals of Robotics
Introduction
History of Robotics
Artificial Intelligence
Robot Positioning
Lesson 2: Introduction to Computer Vision
Introduction
Basic Algorithms in Computer Vision
Introduction to Machine Learning
Lesson 3: Fundamentals of Natural Language Processing
Introduction
NLP in Python
Topic Modeling
Language Modeling
Lesson 4: Neural Networks with NLP
Introduction
Recurrent Neural Networks
Long Short-Term Memory
Neural Language Models
Lesson 5: Convolutional Neural Networks for Computer
Vision
Introduction
Fundamentals of CNNs
Building Your First CNN
Improving Your Model - Data Augmentation
State-of-the-Art Models - Transfer Learning
Lesson 6: Robot Operating System (ROS)
Introduction
ROS Concepts
ROS Commands
Installation and Configuration
Catkin Workspaces and Packages
Publishers and Subscribers
Simulators
Lesson 7: Build a Text-Based Dialogue System (Chatbot)
Introduction
Word Representation in Vector Space
Dialogue Systems
Lesson 8: Object Recognition to Guide a Robot Using CNNs
Introduction
Multiple Object Recognition and Detection
Multiple Object Recognition and Detection in Video
Lesson 9: Computer Vision for Robotics
Introduction
Darknet
YOLO