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Found 39 papers, 17 papers with code
Parameter Filter-based Event-triggered Learning
However, they usually come with a critical assumption - access to an accurate model of the system.
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i. i. d.
GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems
Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage.
Local policy search with Bayesian optimization
We develop an algorithm utilizing a probabilistic model of the objective function and its gradient.
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.
Safe Value Functions
Safety constraints and optimality are important, but sometimes conflicting criteria for controllers.
On exploration requirements for learning safety constraints
In particular, we discuss the family of constraints that enforce safety in the context of a nominal control policy, and expose that these constraints do not need to be accurate everywhere.
Probabilistic robust linear quadratic regulators with Gaussian processes
Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design.
Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees
The combination of machine learning with control offers many opportunities, in particular for robust control.
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression
However, these estimates are of a Bayesian nature, whereas for some important applications, like learning-based control with safety guarantees, frequentist uncertainty bounds are required.
Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload
An important class of cyber-physical systems relies on multiple agents that jointly perform a task by coordinating their actions over a wireless network.
Structure-preserving Gaussian Process Dynamics
On the other hand, classical numerical integrators are specifically designed to preserve these crucial properties through time.
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective
Accurate models of mechanical system dynamics are often critical for model-based control and reinforcement learning.
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.
Learning Event-triggered Control from Data through Joint Optimization
We present a framework for model-free learning of event-triggered control strategies.
Learning Constrained Dynamics with Gauss' Principle adhering Gaussian Processes
The identification of the constrained dynamics of mechanical systems is often challenging.
Identifying Causal Structure in Dynamical Systems
We present a method for automatically identifying the causal structure of a dynamical control system.
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.
A Kernel Two-sample Test for Dynamical Systems
Evaluating whether data streams were generated by the same distribution is at the heart of many machine learning problems, e. g. to detect changes.
Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes
The identification of the constrained dynamics of mechanical systems is often challenging.
Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
Fast feedback control and safety guarantees are essential in modern robotics.
Actively Learning Gaussian Process Dynamics
Despite the availability of ever more data enabled through modern sensor and computer technology, it still remains an open problem to learn dynamical systems in a sample-efficient way.
Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization
In reinforcement learning (RL), an autonomous agent learns to perform complex tasks by maximizing an exogenous reward signal while interacting with its environment.
A Learnable Safety Measure
While safety can only be guaranteed after learning the safety measure, we show that failures can already be greatly reduced by using the estimated measure during learning.
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.
Trajectory-Based Off-Policy Deep Reinforcement Learning
Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks.
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.
Deep Reinforcement Learning for Event-Triggered Control
Event-triggered control (ETC) methods can achieve high-performance control with a significantly lower number of samples compared to usual, time-triggered methods.
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.
Learning an Approximate Model Predictive Controller with Guarantees
A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction.
A Local Information Criterion for Dynamical Systems
Apart from its application for encoding a sequence of observations, we propose to use the compression achieved by this encoding as a criterion for model selection.
Event-triggered Learning for Resource-efficient Networked Control
Common event-triggered state estimation (ETSE) algorithms save communication in networked control systems by predicting agents' behavior, and transmitting updates only when the predictions deviate significantly.
Probabilistic Recurrent State-Space Models
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification.
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.
Depth-Based Object Tracking Using a Robust Gaussian Filter
To address this issue, we show how a recently published robustification method for Gaussian filters can be applied to the problem at hand.
Robust Gaussian Filtering using a Pseudo Measurement
The contribution of this paper is to show that any Gaussian filter can be made compatible with fat-tailed sensor models by applying one simple change: Instead of filtering with the physical measurement, we propose to filter with a pseudo measurement obtained by applying a feature function to the physical measurement.
