Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Unmanned Aerial Vehicles (UAVs) have witnessed remarkable significance in diverse
sectors, ranging from environmental monitoring, infrastructure inspection, disaster response …

Reinforcement learning (RL) for continuous state/action space systems has remained a
challenge for nonlinear multivariate dynamical systems even at a simulation level …

We study the adaptive control of an unknown linear system with a quadratic cost function
subject to safety constraints on both the states and actions. The challenges of this problem …

We study online adaptive policy selection in systems with time-varying costs and dynamics.
We develop the Gradient-based Adaptive Policy Selection (GAPS) algorithm together with a …

Bayesian inference has many advantages in robotic motion planning over four perspectives:
The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees of …

Abstract We propose Kernel Predictive Control (KPC), a learning-based predictive control
strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the …

Robots operating in unstructured, complex, and changing real-world environments should
navigate and maintain safety while collecting data about its environment and updating its …

This paper considers a single-trajectory system identification problem for linear systems
under general nonlinear and/or time-varying policies with iid random excitation noises. The …

The exploration–exploitation tradeoff is an inherent challenge in data-driven adaptive
control. Though this tradeoff has been studied for multiarmed bandits (MABs) and …