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 Duration 21 hours

Course Outline

Foundations of Physical AI and Robotics

  • Overview of Physical AI concepts and their historical evolution
  • Applications extending beyond industrial automation
  • Essential components of intelligent robotic architectures

Robotics System Architecture

  • Mechanical design principles applied to robotics
  • Integration strategies for sensors and actuators
  • Power management and energy efficiency considerations

AI Models for Robotic Intelligence

  • Leveraging machine learning for perception and decision logic
  • Application of reinforcement learning in robotic contexts
  • Constructing robust AI pipelines for robotic systems

Real-Time Sensor Integration

  • Advanced sensor fusion methodologies
  • Processing inputs from LiDAR, cameras, and other sensing devices
  • Implementing real-time navigation and obstacle avoidance strategies

Simulation and Validation

  • Utilizing simulation platforms such as Gazebo and the MATLAB Robotics Toolbox
  • Modeling complex and dynamic environmental conditions
  • Evaluating performance and optimizing system efficiency

Automation and Deployment Strategies

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

Advanced Topics and Emerging Trends

  • Collaborative robots (cobots) and human-robot interaction dynamics
  • Ethical frameworks and regulatory considerations in robotics
  • Future trajectories of Physical AI in automation

Requirements

  • Fundamental understanding of robotics and automation systems
  • Strong programming proficiency, particularly in Python
  • Basic knowledge of AI fundamentals

Target Audience

  • Robotics engineers
  • Automation specialists
  • AI developers

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