Physical AI for Robotics and Automation Training Course

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

By the conclusion of this program, participants will be able to:

• Comprehend the foundational concepts of Human-Centric Physical AI and their real-world uses.
• Evaluate the role of collaborative robots in driving workplace productivity.
• Identify and resolve challenges related to human-machine interaction.
• Develop workflows that enhance cooperation between humans and AI systems.
• Cultivate a culture of innovation and adaptability in workplaces adopting AI technologies.

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

This instructor-led live training in Brazil (online or onsite) is designed for engineers who wish to learn about the applicability 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 Brazil (online or onsite) targets advanced robotics engineers and AI researchers aiming to leverage Multimodal AI to integrate diverse sensory data, thereby creating more autonomous and efficient robots with capabilities in vision, hearing, and touch.

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.
• Build robots capable of executing complex tasks in dynamic environments.
• Overcome challenges related to real-time data processing and actuation.

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