319 citations · 1.2k across the 28 of their papers we have counts for
3 papers · 1 filter
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization
Alonso Marco, Felix Berkenkamp, Philipp Hennig +4
In practice, the parameters of control policies are often tuned manually. This is time-consuming and frustrating. Reinforcement learning is a promising alternative that aims to aut…
Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics
Felix Berkenkamp, Andreas Krause, Angela P. Schoellig
Robotic algorithms typically depend on various parameters, the choice of which significantly affects the robot's performance. While an initial guess for the parameters may be obtai…
Safe Controller Optimization for Quadrotors with Gaussian Processes
Felix Berkenkamp, Angela P. Schoellig, Andreas Krause
One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters. Typically, a model of the system is used to obtain a…