The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …

The goal of this thesis is to design a learning model predictive controller (LMPC) that allows
multiple agents to race competitively on a predefined race track in real-time. This thesis …

In recent years, both reinforcement learning and learning-based control—as well as the
study of their safety, which is crucial for deployment in real-world robots—have gained …

In this work we address the problem of performing a repetitive task when we have uncertain
observations and dynamics. We formulate this problem as an iterative infinite horizon …

In this work, we consider the problem of designing a safety filter for a nonlinear uncertain
control system. Our goal is to augment an arbitrary controller with a safety filter such that the …

In recent years, both reinforcement learning and learning-based control—as well as the
study of their safety , which is crucial for deployment in real-world robots—have gained …

In real-world applications, we often require reliable decision making under dynamics
uncertainties using noisy high-dimensional sensory data. Recently, we have seen an increasing …

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 …

Numerous control applications, including robotic systems such as unmanned aerial vehicles
or assistive robots, are expected to guarantee high performance despite being deployed in …

Ensuring safe interactions in human-centric environments requires robots to understand
and adhere to constraints recognized by humans as "common sense" (eg, "moving a cup of …