Artificial Vision and Language Processing for Robotics
Course Description Overview
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