Book Image

Artificial Intelligence for Robotics

By : Francis X. Govers III
Book Image

Artificial Intelligence for Robotics

By: Francis X. Govers III

Overview of this book

Artificial Intelligence for Robotics starts with an introduction to Robot Operating Systems (ROS), Python, robotic fundamentals, and the software and tools that are required to start out with robotics. You will learn robotics concepts that will be useful for making decisions, along with basic navigation skills. As you make your way through the chapters, you will learn about object recognition and genetic algorithms, which will teach your robot to identify and pick up an irregular object. With plenty of use cases throughout, you will explore natural language processing (NLP) and machine learning techniques to further enhance your robot. In the concluding chapters, you will learn about path planning and goal-oriented programming, which will help your robot prioritize tasks. By the end of this book, you will have learned to give your robot an artificial personality using simulated intelligence.
Table of Contents (13 chapters)

Chapter 5, Picking up the Toys

  1. The origin of the Q-learning title is the doctoral thesis of Christopher John Cornish Hellaby Watkins from King’s College, London in May, 1989. Evidently, the Q just stands for “quantity”.
  2. Only pick the Q-states that are relevant and follow-ons to the current state. If one of the states is impossible to reach from the current position, or state, then don’t consider it.
  3. If the learning rate is too small, the training can take a very long time. If the learning rate is too large, the system does not learn a path, but instead “jumps around” and may miss the minimum or optimum solution. If the learning rate is too big, the solution may not converge, or suddenly drop off.
  1. The discount factor works by decreases the reward as the path length gets longer. It is usually a value just short of 1.0 (for example, 0...