4 accepted papers
Autonomous drone racing presents a challenging control problem, requiring real-time decision-making and robust handling of nonlinear system dynamics. While iterative learning model predictive control (LMPC) offers a promising framework for iterative performance improvement, its direct application to
Reinforcement learning (RL) controllers are flexible and performant but rarely guarantee safety. Safety filters impart hard safety guarantees to RL controllers while maintaining flexibility. However, safety filters can cause undesired behaviours due to the separation between the controller and the s
Ensuring safe interactions in human-centric environments requires robots to understand and adhere to constraints recognized by humans as “common sense” (e.g., “<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">moving a cup of water above a laptop is un
In recent years, both reinforcement learning and learning-based control—as well as the study of their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safety</i> , which is crucial for deployment in real-world robots—have gained significant traction.