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09/06/2023
Learning Hybrid Dynamics Models With Simulator-Informed Latent States
Dynamics model learning deals with the task of inferring unknown dynamic...
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09/05/2023
Exact Inference for Continuous-Time Gaussian Process Dynamics
Physical systems can often be described via a continuous-time dynamical ...
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08/11/2023
Scale-Preserving Automatic Concept Extraction (SPACE)
Convolutional Neural Networks (CNN) have become a common choice for indu...
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07/16/2023
Finite element inspired networks: Learning physically-plausible deformable object dynamics from partial observations
The accurate simulation of deformable linear object (DLO) dynamics is ch...
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06/29/2023
Experience Transfer for Robust Direct Data-Driven Control
Learning-based control uses data to design efficient controllers for spe...
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06/06/2023
Scalable Concept Extraction in Industry 4.0
The industry 4.0 is leveraging digital technologies and machine learning...
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04/19/2023
Approximate non-linear model predictive control with safety-augmented neural networks
Model predictive control (MPC) achieves stability and constraint satisfa...
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03/08/2023
Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test
Machine learning and deep learning have been used extensively to classif...
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02/27/2023
Combining Slow and Fast: Complementary Filtering for Dynamics Learning
Modeling an unknown dynamical system is crucial in order to predict the ...
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02/23/2023
Data-Driven Observability Analysis for Nonlinear Stochastic Systems
Distinguishability and, by extension, observability are key properties o...
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08/23/2022
Event-Triggered Time-Varying Bayesian Optimization
We consider the problem of sequentially optimizing a time-varying object...
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08/03/2022
Learning Fast and Precise Pixel-to-Torque Control
In the field, robots often need to operate in unknown and unstructured e...
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07/28/2022
Improving the Performance of Robust Control through Event-Triggered Learning
Robust controllers ensure stability in feedback loops designed under unc...
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07/22/2022
On Controller Tuning with Time-Varying Bayesian Optimization
Changing conditions or environments can cause system dynamics to vary ov...
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07/14/2022
The Wheelbot: A Jumping Reaction Wheel Unicycle
Combining off-the-shelf components with 3Dprinting, the Wheelbot is a sy...
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06/09/2022
ECLAD: Extracting Concepts with Local Aggregated Descriptors
Convolutional neural networks are being increasingly used in critical sy...
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05/25/2022
Learning dynamics from partial observations with structured neural ODEs
Identifying dynamical systems from experimental data is a notably diffic...
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02/12/2022
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Questions in causality, control, and reinforcement learning go beyond th...
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01/24/2022
Scalable Safe Exploration for Global Optimization of Dynamical Systems
Learning optimal control policies directly on physical systems is challe...
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06/22/2021
Local policy search with Bayesian optimization
Reinforcement learning (RL) aims to find an optimal policy by interactio...
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05/27/2021
GoSafe: Globally Optimal Safe Robot Learning
When learning policies for robotic systems from data, safety is a major ...
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05/25/2021
Safe Value Functions
The relationship between safety and optimality in control is not well un...
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05/17/2021
Probabilistic robust linear quadratic regulators with Gaussian processes
Probabilistic models such as Gaussian processes (GPs) are powerful tools...
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05/07/2021
Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees
The combination of machine learning with control offers many opportuniti...
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05/06/2021
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression
Gaussian Process Regression is a popular nonparametric regression method...
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04/16/2021
Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload
An important class of cyber-physical systems relies on multiple agents t...
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02/02/2021
Symplectic Gaussian Process Dynamics
Dynamics model learning is challenging and at the same time an active fi...
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12/11/2020
Structured learning of rigid-body dynamics: A survey and unified view
Accurate models of mechanical system dynamics are often critical for mod...
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10/18/2020
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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10/16/2020
Robot Learning with Crash Constraints
In the past decade, numerous machine learning algorithms have been shown...
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08/11/2020
Learning Event-triggered Control from Data through Joint Optimization
We present a framework for model-free learning of event-triggered contro...
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06/06/2020
Identifying Causal Structure in Dynamical Systems
We present a method for automatically identifying the causal structure o...
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05/15/2020
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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04/23/2020
Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes
The identification of the constrained dynamics of mechanical systems is ...
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04/23/2020
A Kernel Two-sample Test for Dynamical Systems
A kernel two-sample test is developed for deciding whether two dynamical...
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12/22/2019
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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11/22/2019
Actively Learning Gaussian Process Dynamics
Despite the availability of ever more data enabled through modern sensor...
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10/29/2019
Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization
In reinforcement learning (RL), an autonomous agent learns to perform co...
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10/07/2019
A Learnable Safety Measure
Failures are challenging for learning to control physical systems since ...
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09/19/2019
Fast Feedback Control over Multi-hop 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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07/24/2019
Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties
Learning robot controllers by minimizing a black-box objective cost usin...
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06/13/2019
Demo Abstract: Fast Feedback Control and Coordination with Mode Changes for Wireless Cyber-Physical Systems
This abstract describes the first public demonstration of feedback contr...
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06/08/2019
Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems
In future autonomous systems, wireless multi-hop communication is key to...
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05/14/2019
Trajectory-Based Off-Policy Deep Reinforcement Learning
Policy gradient methods are powerful reinforcement learning algorithms a...
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01/19/2019
Resource-aware IoT Control: Saving Communication through Predictive Triggering
The Internet of Things (IoT) interconnects multiple physical devices in ...
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12/15/2018
Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
Bayesian optimization is proposed for automatic learning of optimal cont...
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09/13/2018
Deep Reinforcement Learning for Event-Triggered Control
Event-triggered control (ETC) methods can achieve high-performance contr...
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09/10/2018
Gait learning for soft microrobots controlled by light fields
Soft microrobots based on photoresponsive materials and controlled by li...
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06/11/2018
Learning an Approximate Model Predictive Controller with Guarantees
A supervised learning framework is proposed to approximate a model predi...
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05/27/2018