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Course Outline

Introduction to Physical AI and Robotics

  • An overview of Physical AI and its evolutionary trajectory
  • Applications spanning industrial automation and beyond
  • Essential components that define intelligent robotic systems

Robotics System Design

  • Mechanical design principles applied to robotics
  • The integration of sensors and actuators
  • Power systems and strategies for energy efficiency

AI Models for Robotics

  • Applying machine learning for perception and decision-making
  • The role of reinforcement learning in robotics
  • Constructing effective AI pipelines for robotic systems

Real-Time Sensor Integration

  • Techniques for effective sensor fusion
  • Processing data streams from LiDAR, cameras, and other sensing devices
  • Implementing real-time navigation and obstacle avoidance mechanisms

Simulation and Testing

  • Utilizing simulation platforms such as Gazebo and the MATLAB Robotics Toolbox
  • Modeling complex dynamic environments
  • Evaluating performance and refining system optimization

Automation and Deployment

  • Programming robots for industrial automation tasks
  • Developing efficient workflows for repetitive operations
  • Safeguarding safety and reliability during deployment

Advanced Topics and Future Trends

  • Collaborative robots (cobots) and the dynamics of human-robot interaction
  • Ethical frameworks and regulatory considerations in robotics
  • Forecasting the future landscape of Physical AI in automation

Requirements

  • Fundamental understanding of robotics and automation systems
  • Programming proficiency, with a preference for Python
  • A solid foundation in AI fundamentals

Target Audience

  • Robotics engineers
  • Automation specialists
  • AI developers
 21 Hours

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