Search Results for author:
Found 11 papers, 3 papers with code
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models
In this paper, we propose (i) a novel approach to embed existing domain knowledge in the kernel and (ii) an OoD online runtime monitor, based on receding-horizon predictions.
GoSafe: Globally Optimal Safe Robot Learning
When learning policies for robotic systems from data, safety is a major concern, as violation of safety constraints may cause hardware damage.
Robot Learning with Crash Constraints
We consider failing behaviors as those that violate a constraint and address the problem of learning with crash constraints, where no data is obtained upon constraint violation.
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures
When learning to ride a bike, a child falls down a number of times before achieving the first success.
Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties
Learning robot controllers by minimizing a black-box objective cost using Bayesian optimization (BO) can be time-consuming and challenging.
Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data.
Gait learning for soft microrobots controlled by light fields
Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits.
On the Design of LQR Kernels for Efficient Controller Learning
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard.
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
PID control architectures are widely used in industrial applications.
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization
In practice, the parameters of control policies are often tuned manually.
Automatic LQR Tuning Based on Gaussian Process Global Optimization
With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data.
