This paper presents a data-driven strategy to streamline the deployment of model-based
controllers in legged robotic hardware platforms. Our approach leverages a model-free safe …

Safety is a fundamental property for the real-world deployment of robotic platforms. Any
control policy should avoid dangerous actions that could harm the environment, humans, or …

We study a generalization of classical active learning to real-world settings with concrete
prediction targets where sampling is restricted to an accessible region of the domain, while …

Integrating learning-based techniques, especially reinforcement learning, into robotics is
promising for solving complex problems in unstructured environments. However, most …

Bayesian optimization (BO) has proven to be a powerful tool for automatically tuning control
parameters without requiring knowledge of the underlying system dynamics. Safe BO …

We consider the problem of sequentially maximizing an unknown function $ f $ over a set of
actions of the form $(s, x) $, where the selected actions must satisfy a safety constraint with …

Optimization-based controller tuning is challenging because it requires formulating
optimization problems explicitly as functions of controller parameters. Safe learning …

Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy
with exploring new options that could lead to higher rewards. Most common RL algorithms …

Reinforcement learning (RL) is ubiquitous in the development of modern AI systems.
However, state-of-the-art RL agents require extensive, and potentially unsafe, interactions …

We consider the problem of sequentially maximising an unknown function over a set of
actions while ensuring that every sampled point has a function value below a given safety …