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Mo Chen – AI to Create Safe and Practical Robotics
Simon Fraser University’s School of Computing Science professor Mo Chen is developing artificial intelligence systems that can more efficiently and safely collect larger volumes of data. The goal is to improve virtual simulations of everyday scenarios with applications for a variety of human-centred autonomous agents including autonomous cars, service robots, and virtual assistants that help people in their daily lives. As a B.C Knowledge Development Fund recipient, Chen hopes to address bottlenecks and enable higher-quality and higher-quantity data collection, leading to a self-sustaining cycle of improved data collection and generation.
Chen’s research interests include multi-agent systems, safety-critical systems, and practical robotics. With an interest in both robotics and visual computing, his team is investigating how to improve virtual 3D simulations of "everyday" scenarios such as shopping in a store or visiting a coffee shop. They plan to use these simulations to develop artificial intelligence for robots and intelligent assistants on mobile devices, so that they can efficiently and safely help people in similar real-world scenarios and further improve 3D simulations.
His work focuses on ensuring that robots are safe while maintaining their usefulness so that people can trust them. Chen believes that we will see robots gradually integrate into society as people become more comfortable with them and sees many ways that robots can have a positive impact on society. This could include having a robot greet you and help you find items in a store, using drones to help film movies or using robots to gather agricultural data to help people understand the well-being of crops.
Chen directs the Multi-Agent Robotic Systems Lab, incorporating prior knowledge and understanding of robotic systems into decision making algorithms to make robots safer and smarter, and enable more widespread use of robotic systems such as autonomous cars, unmanned aerial vehicles, and medical robots. In addition, he is a Canada CIFAR Chair and part of the Natural Sciences and Engineering Research Council of Canada Canadian Robotics Network, an initiative focused on robotic technologies with substantial commercialization potential. CIFAR AI Chairs work to advance research in a wide range of areas, including machine learning for health and responsible AI.
Finding ways to integrate useful and safe robots into society in a way that assists people is an ongoing process that involves working on theory and simulations before experimenting with actual robots. Although robots are designed by people, getting robots to perform useful tasks in a safe manner is an ongoing challenge. In order to teach robots how to perform their desired tasks, researchers like Chen and his team design artificial intelligence algorithms to satisfy performance and safety requirements. For these goals to become a reality, however, there is still a lot of research and testing that needs to be done.
“Theory is very important for designing algorithms that are guaranteed to be safe and for making learning algorithms more efficient,” says Chen. “To test our theory, we need to do a lot of simulations to make sure that the algorithm is doing what it should be doing."
"The next challenge is taking what the robots have done in simulations and transferring it to the real world. A lot of times we find that there are differences in the real world from simulation, so we often have to go back and refine what we developed.”
Chen is excited to continue collaborating with other researchers and to be among a community of well-known researchers who engages in collaborations that combine different research areas together.
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