Our task for this chapter was to use machine learning to teach the robot how to use its robot arm. We used two techniques with some variations. We used a variety of reinforcement learning, called Q-learning, to develop a movement path by selecting individual actions based on the robot's arm state. Each motion was scored individually as a reward, and as part of the overall path as a value. The process stored the results of learning into a Q-matrix that could be used to generate a path. We improved our first cut at the reinforcement learning program by indexing, or encoding, the motions from a 27-element array of possible combinations of motors to a number from 0 to 26, and likewise indexing the robot state to a state lookup table. This resulted in a 40x speedup of the learning process. Our Q-learning approach struggled with the large number of states that the...
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Book Overview & Buying
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Table Of Contents
Artificial Intelligence for Robotics
By :
Artificial Intelligence for Robotics
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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)
Preface
Free Chapter
Foundation for Advanced Robotics and AI
Setting Up Your Robot
A Concept for a Practical Robot Design Process
Object Recognition Using Neural Networks and Supervised Learning
Picking up the Toys
Teaching a Robot to Listen
Avoiding the Stairs
Putting Things Away
Giving the Robot an Artificial Personality
Conclusions and Reflections
Assessments
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