Stability and safety are critical properties for successful deployment of automatic control
systems. As a motivating example, consider autonomous mobile robot navigation in a …

Safe autonomous navigation in unknown environments is an important problem for mobile
robots. This paper proposes techniques to learn the dynamics model of a mobile robot from …

Abstract The Neural Network (NN), as a black-box function approximator, has been
considered in many control and robotics applications. However, difficulties in verifying the …

This dissertation explores the integration of machine learning into feedback control systems,
addressing key challenges in the realm of control theory with a focus on autonomous …

Learning-based control algorithms have led to major advances in robotics at the cost of
decreased safety guarantees. Recently, neural networks have also been used to …

Agile flights of autonomous quadrotors in clut-tered environments require constrained
motion planning and control subject to translational and rotational dynamics. Tra-ditional …

Hybrid systems are prevalent in robotics. However, ensuring the stability of hybrid systems is
challenging due to sophisticated continuous and discrete dynamics. A system with all its …

Adaptive control is a critical component of reliable robot autonomy in rapidly changing
operational conditions. Adaptive control designs benefit from a disturbance model, which is …

The proven efficacy of learning-based control schemes strongly motivates their application
to robotic systems operating in the physical world. However, guaranteeing correct operation …

Providing safety guarantees for learning-based controllers is important for real-world
applications. One approach to realizing safety for arbitrary control policies is safety filtering. If …