Physical AI for Robotics and Automation Training Course

This instructor-led, live training in Argentina (online or onsite) is designed for intermediate-level participants eager to explore the role of collaborative robots (cobots) and other human-centric AI systems in modern workplaces.

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

• Grasp the fundamental principles of Human-Centric Physical AI and its practical applications.
• Examine how collaborative robots enhance workplace productivity.
• Identify and address challenges inherent in human-machine interactions.
• Design workflows that maximize collaboration between humans and AI-driven systems.
• Foster a culture of innovation and adaptability within AI-integrated workplaces.

This practical training in Argentina focuses on ROS-PLC integration and digital twins for industrial automation. Participants will learn to synchronize robotic systems with PLCs, create digital replicas for simulation, and streamline production workflows through real-time data exchange and advanced monitoring techniques.

This instructor-led, live training in Argentina (online or onsite) is designed for engineers interested in understanding how artificial intelligence can be applied to mechatronic systems.

Upon completing this training, participants will be able to:

• Obtain a comprehensive overview of artificial intelligence, machine learning, and computational intelligence.
• Grasp the fundamental concepts of neural networks and various learning methodologies.
• Select appropriate artificial intelligence approaches to effectively address real-world challenges.
• Implement AI solutions within the context of mechatronic engineering.

This instructor-led, live training in Argentina (online or onsite) is designed for advanced robotics engineers and AI researchers aiming to utilize Multimodal AI to integrate various sensory data. The objective is to create more autonomous and efficient robots capable of seeing, hearing, and touching.

By the end of this training, 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 Argentina covering deep learning for robotic manipulation. Learn perception, grasp planning, and control using neural networks and ROS 2. Hands-on exercises in simulation prepare you to deploy intelligent, adaptable grasping solutions on real robotic arms effectively.