Dr Kai Yuan
In traditional robot control, algorithms are programmed based on a specific task, which means that if the robot encounters a new scenario that it is not programmed for, it has difficulties to handle novel situations. This greatly reduces the real world applications. To improve the robustness and generality of robot programmes, and thus the capabilities of robots, my research explores ways to combine classical optimal control methods with gradient-free methods from Machine Learning, such as Deep Reinforcement Learning and Bayesian Optimisation. A hierarchical control framework allows the combination of classical control on the lower levels to control the actuators of the robot achieving stability and balance control and Machine Learning on the higher-levels of control for decision making and planning. Using Machine Learning for the decision making and planning enables the robot to behave more intelligent, dynamically and animal-like, while the control algorithms for the lower levels maintain the robustness and stability properties of classical control.
I have completed my PhD studies at the Edinburgh Centre for Robotics under the supervision of Dr. Zhibin Li and Professor Timothy Hospedales. My main interest lies in the application of technology (e.g., Machine Learning, Robotics) to solve real-world problems. I hold an MSc with Distinction in Robotics and Autonomous Systems from The University of Edinburgh, and an MSc & BSc in Engineering Cybernetics from the University of Stuttgart (Stuttgart, Germany). Throughout and prior to my PhD, I was fortunate to gather experience in various institutions such as Amazon (Robotics & AI), Roland Berger (Management Consulting), Tsinghua University (Research), UBTECH Robotics (Algorithm development), and Bosch (Corporate Research).
* indicates equal contribution and co-first authors