
Local policy search with Bayesian optimization
Reinforcement learning (RL) aims to find an optimal policy by interactio...
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GoSafe: Globally Optimal Safe Robot Learning
When learning policies for robotic systems from data, safety is a major ...
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Safe Value Functions
The relationship between safety and optimality in control is not well un...
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Probabilistic robust linear quadratic regulators with Gaussian processes
Probabilistic models such as Gaussian processes (GPs) are powerful tools...
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Learningenhanced robust controller synthesis with rigorous statistical and controltheoretic guarantees
The combination of machine learning with control offers many opportuniti...
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Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression
Gaussian Process Regression is a popular nonparametric regression method...
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Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload
An important class of cyberphysical systems relies on multiple agents t...
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Symplectic Gaussian Process Dynamics
Dynamics model learning is challenging and at the same time an active fi...
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Structured learning of rigidbody dynamics: A survey and unified view
Accurate models of mechanical system dynamics are often critical for mod...
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Wireless Control for Smart Manufacturing: Recent Approaches and Open Challenges
Smart manufacturing aims to overcome the limitations of today's rigid as...
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Robot Learning with Crash Constraints
In the past decade, numerous machine learning algorithms have been shown...
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Learning Eventtriggered Control from Data through Joint Optimization
We present a framework for modelfree learning of eventtriggered contro...
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Identifying Causal Structure in Dynamical Systems
We present a method for automatically identifying the causal structure o...
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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 befor...
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Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes
The identification of the constrained dynamics of mechanical systems is ...
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A Kernel Twosample Test for Dynamical Systems
A kernel twosample test is developed for deciding whether two dynamical...
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Safe and Fast Tracking Control on a Robot Manipulator: Robust MPC and Neural Network Control
Fast feedback control and safety guarantees are essential in modern robo...
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Actively Learning Gaussian Process Dynamics
Despite the availability of ever more data enabled through modern sensor...
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Robust Modelfree Reinforcement Learning with Multiobjective Bayesian Optimization
In reinforcement learning (RL), an autonomous agent learns to perform co...
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A Learnable Safety Measure
Failures are challenging for learning to control physical systems since ...
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Fast Feedback Control over Multihop Wireless Networks with Mode Changes and Stability Guarantees
Closing feedback loops fast and over long distances is key to emerging c...
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Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties
Learning robot controllers by minimizing a blackbox objective cost usin...
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Demo Abstract: Fast Feedback Control and Coordination with Mode Changes for Wireless CyberPhysical Systems
This abstract describes the first public demonstration of feedback contr...
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Controlguided Communication: Efficient Resource Arbitration and Allocation in Multihop Wireless Control Systems
In future autonomous systems, wireless multihop communication is key to...
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TrajectoryBased OffPolicy Deep Reinforcement Learning
Policy gradient methods are powerful reinforcement learning algorithms a...
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Resourceaware IoT Control: Saving Communication through Predictive Triggering
The Internet of Things (IoT) interconnects multiple physical devices in ...
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Dataefficient Autotuning with Bayesian Optimization: An Industrial Control Study
Bayesian optimization is proposed for automatic learning of optimal cont...
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Deep Reinforcement Learning for EventTriggered Control
Eventtriggered control (ETC) methods can achieve highperformance contr...
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Gait learning for soft microrobots controlled by light fields
Soft microrobots based on photoresponsive materials and controlled by li...
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Learning an Approximate Model Predictive Controller with Guarantees
A supervised learning framework is proposed to approximate a model predi...
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A Local Information Criterion for Dynamical Systems
Encoding a sequence of observations is an essential task with many appli...
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Evaluating LowPower Wireless CyberPhysical Systems
Simulation tools and testbeds have been proposed to assess the performan...
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Feedback Control Goes Wireless: Guaranteed Stability over Lowpower Multihop Networks
Closing feedback loops fast and over long distances is key to emerging a...
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Eventtriggered Learning for Resourceefficient Networked Control
Common eventtriggered state estimation (ETSE) algorithms save communica...
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Probabilistic Recurrent StateSpace Models
Statespace models (SSMs) are a highly expressive model class for learni...
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On the Design of LQR Kernels for Efficient Controller Learning
Finding optimal feedback controllers for nonlinear dynamic systems from ...
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Distributed EventBased State Estimation for Networked Systems: An LMIApproach
In this work, a dynamic system is controlled by multiple sensoractuator...
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Eventbased State Estimation: An Emulationbased Approach
An eventbased state estimation approach for reducing communication in a...
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ModelBased Policy Search for Automatic Tuning of Multivariate PID Controllers
PID control architectures are widely used in industrial applications. De...
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DepthBased Object Tracking Using a Robust Gaussian Filter
We consider the problem of modelbased 3Dtracking of objects given dens...
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Robust Gaussian Filtering using a Pseudo Measurement
Many sensors, such as range, sonar, radar, GPS and visual devices, produ...
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Sebastian Trimpe
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