Master Class: Artificial Intelligence and The Power of Simulation
Simulation has become a fundamental practice for development in Robotics Industry and is reaching new heights with trends such as Digital Twins. But how can the power of simulation be combined with the impressive models offered by Artificial Intelligence?
In this session, we will present four growing trends in Robotics Industry that combine AI and Simulation. We will go beyond the mere use of AI models in Simulink, showing an unknown potential in many cases.
- AI for Reduced Order Models (ROM) - a way to manage the computational complexity of high-fidelity models.
- AI-based Virtual Sensors - emulating a physical sensor where measurement is not possible.
- System Control based on Reinforcement Learning - let the model learn by itself in a simulated environment. AI integration in Simulink - simple and effective.
- Improving AI models using Digital Twins - diversify your dataset using simulation.
Who Should Attend
All ROBOT 2023 attendees.
About the Presenter
Miguel Alonso is a member of the application engineering team at MathWorks. His role in the company is to provide technical support to customers in the field of Data Analytics and Artificial Intelligence. Prior to joining MathWorks, Miguel gained work experience in different industry sectors, excelling in the development of autonomous navigation systems, computer vision and mechatronic models. His skills and knowledge derive from his background in industrial engineering, which he studied at the Polytechnic University of Madrid, and from his specialization in Robotics and Mechatronics at the University of Twente in the Netherlands.
Jennifer J. Gago is a robotics engineer working in the MathWorks academic group as a technical specialist. She is responsible for providing solutions to university professors and researchers to include computational thinking and Model-Based Design in their activities. Prior to joining MathWorks in 2019, she worked with portable radiography equipment as an industry researcher. She also worked in academia researching on the use of anthropomorphic manipulators and AI for automated sign language learning by humanoid robots. Jennifer holds a bachelor’s degree in industrial engineering with a specialization in electronics and automation and a master’s degree in robotics and automation from University Carlos III of Madrid. She is currently completing a distance learning degree in philosophy.