Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead
of manually designing costs based on terrain features, existing methods learn terrain …

Today's control engineering problems exhibit an unprecedented complexity, with examples
including the reliable integration of renewable energy sources into power grids, safe …

Learning-based approaches are emerging as an effective approach for safety filters for black-
box dynamical systems. Existing methods have relied on certificate functions like Control …

In the context of safe exploration, Reinforcement Learning (RL) has long grappled with the
challenges of balancing the tradeoff between maximizing rewards and minimizing safety …

Learning from Demonstration (LfD) is a powerful method for non-roboticists end-users to
teach robots new tasks, enabling them to customize the robot behavior. However, modern …

Safety and stability are two primary factors that affect the task execution performance of
autonomous underwater vehicles (AUVs), especially when they operate in a disturbed …

End-to-end vision-based autonomous driving has achieved impressive success, but safety
remains a major concern. The safe control problem has been addressed in low-dimensional …

Safety filters in control systems correct nominal controls that violate safety constraints.
Designing such filters as functions of visual observations in uncertain and complex …

Recently, the real-world applications of reinforcement learning (RL) have seen the problem
of taking actions in an out-of-distribution (OOD) state. However, most existing research is …

Task scheduling, path planning, and control are all problems in robotics that involve
choosing the best action to take given the current state of the system. For task scheduling …