Welcome to the channel of SUSTech Control & Learning for Robotics and Autonomy (CLEAR) Lab. Our lab develops new theoretic and algorithmic tools in control and learning theory to enable advanced applications in modern robotic and autonomous systems. Our research crosscuts various areas, including underwater robotics, legged locomotion control, autonomous system navigation and collision avoidance, dynamic manipulation, UAV control, among others. Although the practical contexts of these areas appear to be different, the underlying research questions have a lot in common: (1) how to obtain a good dynamic model (rigid-body dynamics, system identification); (2) how to design a controller to achieve a desired dynamic performance (model predictive control, optimal control); (3) how to effectively interact with the environment and other robots (motion planning, reinforcement learning, collision avoidance).

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