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Course Outline

Introduction to Physical AI and Robotics

  • Overview of Physical AI concepts and their evolution
  • Applications in industrial automation and broader sectors
  • Essential components of intelligent robotic systems

Robotics System Design

  • Mechanical design fundamentals for robotic structures
  • Integrating sensors and actuators
  • Power system architecture and energy efficiency strategies

AI Models for Robotics

  • Applying machine learning for perception and decision-making
  • Reinforcement learning techniques in robotics
  • Constructing AI pipelines specifically for robotic systems

Real-Time Sensor Integration

  • Advanced sensor fusion techniques
  • Processing data streams from LiDAR, cameras, and other sensing hardware
  • Real-time navigation and obstacle avoidance strategies

Simulation and Testing

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

Automation and Deployment

  • Programming robots for industrial automation tasks
  • Developing efficient workflows for repetitive operations
  • Ensuring safety and reliability standards in deployment

Advanced Topics and Future Trends

  • Collaborative robots (cobots) and human-robot interaction
  • Ethical frameworks and regulatory considerations in robotics
  • The future trajectory of Physical AI in automation

Requirements

  • Foundational understanding of robotics and automation systems
  • Strong programming proficiency, with Python preferred
  • Basic familiarity with AI concepts

Target Audience

  • Robotics Engineers
  • Automation Specialists
  • AI Developers
 21 Hours

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