Physical AI for Robotics and Automation Training Course

This instructor-led, live training in Greece (offered online or onsite) is designed for intermediate-level learners interested in exploring the role of collaborative robots (cobots) and other human-centric AI systems in modern workplaces.

Upon completing this training, participants will be able to:

• Understand the core principles of Human-Centric Physical AI and their applications.
• Explore the role of collaborative robots in enhancing workplace productivity.
• Identify and address challenges in human-machine interactions.
• Design workflows that optimize collaboration between humans and AI-driven systems.
• Promote a culture of innovation and adaptability in AI-integrated workplaces.

This practical training module, Greece, addresses ROS-PLC integration and digital twins within industrial automation. Participants will learn to synchronize robotic systems with PLCs, construct digital replicas for simulation purposes, and refine production workflows by leveraging real-time data exchange and sophisticated monitoring methodologies.

This instructor-led live training in Greece (available online or onsite) is designed for engineers who wish to explore the application of artificial intelligence to mechatronic systems.

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

• Gain an overview of artificial intelligence, machine learning, and computational intelligence.
• Understand the concepts of neural networks and different learning methods.
• Choose artificial intelligence approaches effectively for real-life problems.
• Implement AI applications in mechatronic engineering.

This instructor-led, live training in Greece (online or onsite) targets advanced robotics engineers and AI researchers looking to leverage Multimodal AI. The course focuses on integrating diverse sensory data to build more autonomous and efficient robots capable of seeing, hearing, and touching.

Upon completion of this training, participants will be able to:

• Implement multimodal sensing within robotic systems.
• Develop AI algorithms for sensor fusion and decision-making processes.
• Build robots capable of executing complex tasks in dynamic environments.
• Address challenges related to real-time data processing and actuation.

An advanced training in Greece focused on applying deep learning to robotic manipulation. Master perception, grasp planning, and control using neural networks and ROS 2. Practical simulation exercises prepare you to effectively deploy intelligent, adaptable grasping solutions on real robotic arms.