17 citations · 29 across the 14 of their papers we have counts for
17 papers
Learning Safe Control via On-the-Fly Bandit Exploration
Alexandre Capone, Ryan Cosner, Aaaron Ames +1
Control tasks with safety requirements under high levels of model uncertainty are increasingly common. Machine learning techniques are frequently used to address such tasks, typica…
Koopman-Equivariant Gaussian Processes
Petar Bevanda, Max Beier, Armin Lederer +3
Credible forecasting and representation learning of dynamical systems are of ever-increasing importance for reliable decision-making. To that end, we propose a family of Gaussian p…
Computation-Aware Learning for Stable Control with Gaussian Process
Wenhan Cao, Alexandre Capone, Rishabh Yadav +2
In Gaussian Process (GP) dynamical model learning for robot control, particularly for systems constrained by computational resources like small quadrotors equipped with low-end pro…
Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions
Tzu-Yuan Huang, Sihua Zhang, Xiaobing Dai +4
In many control system applications, state constraint satisfaction needs to be guaranteed within a prescribed time. While this issue has been partially addressed for systems with k…
Analyzing the Impact of Computation in Adaptive Dynamic Programming for Stochastic LQR Problem
Wenhan Cao, Alexandre Capone, Sandra Hirche +1
Adaptive dynamic programming (ADP) for stochastic linear quadratic regulation (LQR) demands the precise computation of stochastic integrals during policy iteration (PI). In a fully…
Safe Online Dynamics Learning with Initially Unknown Models and Infeasible Safety Certificates
Alexandre Capone, Ryan Cosner, Aaron Ames +1
Safety-critical control tasks with high levels of uncertainty are becoming increasingly common. Typically, techniques that guarantee safety during learning and control utilize cons…