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Control and Learning for Autonomous Robots (CleAR) Research Group
Assistant Professor Erkan Kayacan
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Research Vision
Our goal is to develop learning-based algorithms and design novel autonomous robots that can be implemented easily in real-world systems and operated safely and effectively in real-world scenarios.
Core Research Areas
Our goal is to develop learning-based algorithms and design novel autonomous robots that can be implemented easily in real-world systems and operated safely and effectively in real-world scenarios.
Core Research Areas
- Optimization-based algorithms
- Data-based control and estimation methods
- Physics-informed neural networks for autonomous robots
- Security
- Inspection
- Collaborative robotics