Robust Autonomous

Robust autonomous systems aim to create machines capable of reliably operating in unpredictable environments, a challenge addressed through advanced control and planning algorithms. Current research emphasizes data-driven approaches, leveraging machine learning models like Gaussian Processes and Koopman operators to learn complex system dynamics and improve adaptability, often incorporating digital twins for efficient data generation and simulation-to-reality transfer. This work is crucial for advancing applications in diverse fields, including off-road navigation, maritime autonomy, and robotic manipulation, by enhancing safety, efficiency, and resilience in autonomous systems.