Physical AI for Robotics and Automation Training Course

This instructor-led, live training in Taiwan (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 hands-on training on Taiwan explores ROS-PLC integration and digital twins within the context of industrial automation. Participants will learn to synchronize robotic systems with PLCs, create digital replicas for simulation, and enhance production workflows through real-time data exchange and sophisticated monitoring techniques.

This instructor-led live training in Taiwan (online or onsite) is tailored for engineers interested in applying artificial intelligence to mechatronic systems.

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

• Obtain a comprehensive understanding of artificial intelligence, machine learning, and computational intelligence.
• Grasp the fundamental concepts of neural networks and various learning methodologies.
• Effectively select appropriate artificial intelligence approaches for real-world challenges.
• Implement AI solutions within mechatronic engineering contexts.

This instructor-led, live training in Taiwan (online or onsite) is designed for advanced robotics engineers and AI researchers who aim to leverage Multimodal AI to integrate diverse sensory data, creating 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.
• Build robots capable of performing complex tasks in dynamic environments.
• Address challenges associated with real-time data processing and actuation.

An advanced Taiwan training focused on deep learning for robotic manipulation. Master perception, grasp planning, and control through neural networks and ROS 2. Practical simulation exercises prepare you to effectively deploy intelligent, adaptable grasping solutions on physical robotic arms.