Applying AI to Robotics

Artificial Intelligence    |    Expert
  • 17 Videos | 1h 4m 30s
  • Includes Assessment
  • Earns a Badge
Likes 11 Likes 11
Robots can utilize machine learning, deep learning, reinforcement learning, as well as probabilistic techniques to achieve intelligent behavior. This application of AI to robotic systems is found in the automotive, healthcare, logistics, and military industries. With increasing computing power and sophistication in small robots, more industry use cases are likely to emerge, making AI development for robotics a useful AI developer skill. In this course, you'll explore the main concepts, frameworks, and approaches needed to work with robotics and apply AI to robots. You'll examine how AI and robotics are used across multiple industries. You'll learn how to work with commonly used algorithms and strategies to develop simple AI systems that improve the performance of robots. Finally, you'll learn how to control a robot in a simulated environment using deep Q-networks.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    outline a brief history of the robotics industry
    identify the role AI plays in the robotics industry
    describe the concept of a cobot and list multiple cobot use cases
    list possible applications and use cases of AI in the robotics industry
    specify how AI and robotics are used in the automotive industry
    specify how AI and robotics are used in the healthcare industry
    specify how AI and robotics are used in the manufacturing industry
    specify how AI and robotics are used for warehouse automation and logistics
  • specify how AI and robotics are applied in the military
    list open source robotics frameworks and identify the context of their application
    define the role of reinforcement learning in robotics
    give examples of how cognitive and deep learning models are used in the robotics industry
    use common AI tools to improve the navigation of robots across surfaces
    work with deep Q-networks to control a robot in a simulated environment
    configure a robotics AI pipeline using the ROS framework in Jupyter Notebooks
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 45s
    UP NEXT
  • Playable
    2. 
    A History of Robotics
    3m 56s
  • Locked
    3. 
    AI in Robotics
    3m 51s
  • Locked
    4. 
    Collaborative Robots: Cobots
    5m 15s
  • Locked
    5. 
    AI and Robotics: Applications
    1m 26s
  • Locked
    6. 
    AI and Robotics in the Automotive Industry
    3m 24s
  • Locked
    7. 
    AI and Robotics in Healthcare
    3m 9s
  • Locked
    8. 
    AI and Robotics in Manufacturing
    2m 25s
  • Locked
    9. 
    AI and Robotics in Logistics
    2m 20s
  • Locked
    10. 
    AI and Robotics in the Military
    3m 9s
  • Locked
    11. 
    Open Source Robotics
    3m 6s
  • Locked
    12. 
    Reinforcement Learning in Robotics
    4m 46s
  • Locked
    13. 
    Cognitive and Deep Learning Models in Robotics
    3m 8s
  • Locked
    14. 
    Predict Floor Surfaces Using a Robot’s Sensor data
    4m 1s
  • Locked
    15. 
    Deep Reinforcement Learning for Robotics
    4m 38s
  • Locked
    16. 
    ROS and Jupyter Notebooks
    5m 57s
  • Locked
    17. 
    Course Summary
    43s