Physical AI Fundamentals Training is a comprehensive two-day program designed to introduce learners to the principles and practical foundations of Physical AI, where artificial intelligence systems interact with and act within the physical world. This course focuses on the integration of AI with robotics, sensors, autonomous systems, and embodied intelligence, enabling participants to understand how perception, decision-making, and action combine to power real-world intelligent machines.

• Understand Physical AI fundamentals
• Learn basics of robotics and autonomous systems
• Understand sensors and AI perception
• Gain knowledge of computer vision concepts
• Learn AI decision-making fundamentals
• Understand simulation and digital twins
• Identify industrial use cases of Physical AI
• Learn safety and operational considerations

• Robotics and automation engineers
• AI and machine learning professionals
• Embedded systems and IoT developers
• Researchers exploring embodied intelligence
• Technology leaders working on autonomous systems

• Basic understanding of artificial intelligence or machine learning concepts
• Familiarity with robotics, automation, or embedded systems is beneficial
• General awareness of sensors, hardware, or control systems
• Interest in autonomous and intelligent physical systems

• Understanding the core concepts of Physical AI and embodied intelligence
• Awareness of AI perception, planning, and control in physical systems
• Understanding sensor integration and real-time decision-making
• Evaluating safety, ethics, and reliability of Physical AI systems
• Interpreting real-world applications of Physical AI across industries

By the end of this training, participants will be able to explain the fundamentals of Physical AI, understand how AI systems perceive and act in the physical world, evaluate design, safety, and ethical considerations, and assess real-world applications and future trends in embodied and autonomous intelligence.

Introduction to Physical AI and Embodied Intelligence
• Definition and scope of Physical AI
• Difference between digital AI and Physical AI systems
• Role of embodiment in intelligence
• Overview of real-world Physical AI applications

Perception and Sensor Intelligence
• Sensors, data acquisition, and perception pipelines
• Computer vision and sensor fusion concepts
• Environment understanding and localization
• Handling uncertainty in real-world inputs

Decision-Making and Control Systems
• Planning, reasoning, and action selection
• Reinforcement learning in physical environments
• Control strategies for autonomous systems
• Balancing autonomy and human oversight

Learning in Physical Environments
• Simulation versus real-world learning
• Transfer learning from simulation to reality
• Continuous learning and adaptation
• Managing data efficiency and safety

Safety, Ethics, and Reliability
• Safety-critical system design
• Ethical considerations in Physical AI
• Risk management and fail-safe mechanisms
• Regulatory and compliance considerations

Physical AI Use Cases and Future Trends
• Robotics, autonomous vehicles, and drones
• Industrial automation and smart manufacturing
• Healthcare and service robots
• Future directions of Physical AI and embodied systems

• Physical AI concepts
• Robotics fundamentals
• Sensors and perception systems
• Computer vision basics
• Autonomous navigation concepts
• Machine learning fundamentals
• Simulation and digital twins
• Industrial automation use cases
• IoT and Edge AI concepts
• Safety and ethics in Physical AI

• Conceptual understanding assessments
• Case-based analysis of Physical AI systems
• Safety and ethics evaluation exercise
• Final knowledge evaluation quiz

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in Physical AI Fundamentals Training, validating their expertise in understanding Physical AI concepts, embodied intelligence, system design principles, and real-world applications.