5 papers
Controller Design for Bilinear Neural Feedback Loops
Dhruv Shah, Jorge Cortés
This paper considers a class of bilinear systems with a neural network in the loop. These arise naturally when employing machine learning techniques to approximate general, non-aff…
Data-Driven Stabilization of Unknown Linear-Threshold Network Dynamics
Xuan Wang, Duy Duong-Tran, Jorge Cortés
This paper studies the data-driven control of unknown linear-threshold network dynamics to stabilize the state to a reference value. We consider two types of controllers: (i) a sta…
Anytime Safe Reinforcement Learning
Pol Mestres, Arnau Marzabal, Jorge Cortés
This paper considers the problem of solving constrained reinforcement learning problems with anytime guarantees, meaning that the algorithmic solution returns a safe policy regardl…
Control Barrier Function-Based Safety Filters: Characterization of Undesired Equilibria, Unbounded Trajectories, and Limit Cycles
Pol Mestres, Yiting Chen, Emiliano Dall'anese +1
This paper focuses on safety filters designed based on Control Barrier Functions (CBFs): these are modifications of a nominal stabilizing controller typically utilized in safety-cr…
Back to Base: Towards Hands-Off Learning via Safe Resets with Reach-Avoid Safety Filters
Azra Begzadić, Nikhil Uday Shinde, Sander Tonkens +5
Designing controllers that accomplish tasks while guaranteeing safety constraints remains a significant challenge. We often want an agent to perform well in a nominal task, such as…