Physical AI for Robotics and Automation Training Course

This instructor-led, live training in Romania (online or onsite) is aimed at intermediate-level participants who wish to explore the role of collaborative robots (cobots) and other human-centric AI systems in modern workplaces.

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

• Understand the principles of Human-Centric Physical AI and its 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 in Romania addresses ROS-PLC integration and digital twins within industrial automation. Participants will learn to synchronize robotic systems with PLCs, create digital replicas for simulation purposes, and enhance production workflows via real-time data exchange and advanced monitoring techniques.

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

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

• Gain a broad overview of artificial intelligence, machine learning, and computational intelligence.
• Understand the core concepts of neural networks and various learning methods.
• Select the most effective artificial intelligence approaches for solving real-world problems.
• Implement AI applications in the field of mechatronic engineering.

This instructor-led training in Romania (online or onsite) is designed for advanced robotics engineers and AI researchers looking to leverage Multimodal AI. The goal is to integrate diverse sensory data to build more autonomous and efficient robots that can see, hear, and touch.

Upon completion, participants will be able to:

• Implement multimodal sensing in robotic systems.
• Develop AI algorithms for sensor fusion and decision-making.
• Create robots that can perform complex tasks in dynamic environments.
• Address challenges in real-time data processing and actuation.

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