Physical AI for Robotics and Automation

• Basic knowledge of robotics and automation systems
• Proficiency in programming, preferably Python
• Familiarity with AI fundamentals

Audience

• Robotics engineers
• Automation specialists
• AI developers

Physical AI combines artificial intelligence and robotics to create machines capable of autonomous decision-making and interaction with their physical environment.

This instructor-led, live training (online or onsite) is aimed at intermediate-level participants who wish to enhance their skills in designing, programming, and deploying intelligent robotic systems for automation and beyond.

By the end of this training, participants will be able to:

• Understand the principles of Physical AI and its applications in robotics and automation.
• Design and program intelligent robotic systems for dynamic environments.
• Implement AI models for autonomous decision-making in robots.
• Leverage simulation tools for robotic testing and optimization.
• Address challenges such as sensor fusion, real-time processing, and energy efficiency.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Physical AI and Robotics

• Overview of Physical AI and its evolution
• Applications in industrial automation and beyond
• Key components of intelligent robotic systems

Robotics System Design

• Mechanical design principles for robots
• Integration of sensors and actuators
• Power systems and energy efficiency

AI Models for Robotics

• Using machine learning for perception and decision-making
• Reinforcement learning in robotics
• Building AI pipelines for robotic systems

Real-Time Sensor Integration

• Sensor fusion techniques
• Processing data from LiDAR, cameras, and other sensors
• Real-time navigation and obstacle avoidance

Simulation and Testing

• Using simulation tools like Gazebo and MATLAB Robotics Toolbox
• Modeling dynamic environments
• Performance evaluation and optimization

Automation and Deployment

• Programming robots for industrial automation
• Developing workflows for repetitive tasks
• Ensuring safety and reliability in deployments

Advanced Topics and Future Trends

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

Summary and Next Steps