630 citations · 1.2k across the 74 of their papers we have counts for
36 papers · 1 filter
Risk-Sensitive Inhibitory Control for Safe Reinforcement Learning
Armin Lederer, Erfaun Noorani, John S. Baras +1
Humans have the ability to deviate from their natural behavior when necessary, which is a cognitive process called response inhibition. Similar approaches have independently receiv…
Episodic Gaussian Process-Based Learning Control with Vanishing Tracking Errors
Armin Lederer, Jonas Umlauft, Sandra Hirche
Due to the increasing complexity of technical systems, accurate first principle models can often not be obtained. Supervised machine learning can mitigate this issue by inferring m…
Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning
Xiaobing Dai, Armin Lederer, Zewen Yang +1
When the dynamics of systems are unknown, supervised machine learning techniques are commonly employed to infer models from data. Gaussian process (GP) regression is a particularly…
Fast IMU-based Dual Estimation of Human Motion and Kinematic Parameters via Progressive In-Network Computing
Xiaobing Dai, Huanzhuo Wu, Siyi Wang +4
Many applications involve humans in the loop, where continuous and accurate human motion monitoring provides valuable information for safe and intuitive human-machine interaction.…
Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems
Qingchen Liu, Zengjie Zhang, Nhan Khanh Le +3
This paper proposes a novel distributed coverage controller for a multi-agent system with constant-speed unicycle robots (CSUR). The work is motivated by the limitation of the conv…
Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version
Xiaobing Dai, Zewen Yang, Sihua Zhang +3
In the realm of the cooperative control of multi-agent systems (MASs) with unknown dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to its m…
Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States
Robert Lefringhausen, Supitsana Srithasan, Armin Lederer +1
As control engineering methods are applied to increasingly complex systems, data-driven approaches for system identification appear as a promising alternative to physics-based mode…
Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions
Armin Lederer, Azra Begzadić, Neha Das +1
Ensuring safety is of paramount importance in physical human-robot interaction applications. This requires both adherence to safety constraints defined on the system state, as well…
Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes
Armin Lederer, Mingmin Zhang, Samuel Tesfazgi +1
Safety-critical technical systems operating in unknown environments require the ability to quickly adapt their behavior, which can be achieved in control by inferring a model onlin…
Towards Data-driven LQR with Koopmanizing Flows
Petar Bevanda, Max Beier, Shahab Heshmati-Alamdari +2
We propose a novel framework for learning linear time-invariant (LTI) models for a class of continuous-time non-autonomous nonlinear dynamics based on a representation of Koopman o…
Distributed Value of Information in Feedback Control over Multi-hop Networks
Precious Ugo Abara, Sandra Hirche
Recent works in the domain of networked control systems have demonstrated that the joint design of medium access control strategies and control strategies for the closed-loop syste…
Value of information in networked control systems subject to delay
Siyi Wang, Qingchen Liu, Precious Ugo Abara +2
In this paper, we study the trade-off between the transmission cost and the control performance of the multi-loop networked control system subject to network-induced delay. Within…
Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes
Thomas Beckers, Sandra Hirche, Leonardo Colombo
Formation control algorithms for multi-agent systems have gained much attention in the recent years due to the increasing amount of mobile and aerial robotic swarms. The design of…
Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes
Zewen Yang, Stefan Sosnowski, Qingchen Liu +3
In this paper, a distributed learning leader-follower consensus protocol based on Gaussian process regression for a class of nonlinear multi-agent systems with unknown dynamics is…
Koopman Operator Dynamical Models: Learning, Analysis and Control
Petar Bevanda, Stefan Sosnowski, Sandra Hirche
The Koopman operator allows for handling nonlinear systems through a (globally) linear representation. In general, the operator is infinite-dimensional - necessitating finite appro…
The Impact of Data on the Stability of Learning-Based Control- Extended Version
Armin Lederer, Alexandre Capone, Thomas Beckers +2
Despite the existence of formal guarantees for learning-based control approaches, the relationship between data and control performance is still poorly understood. In this paper, w…
Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics
Thomas Beckers, Leonardo Colombo, Sandra Hirche +1
Underactuated vehicles have gained much attention in the recent years due to the increasing amount of aerial and underwater vehicles as well as nanosatellites. Trajectory tracking…
Delay-sensitive Joint Optimal Control and Resource Management in Multi-loop Networked Control Systems
Mohammad H. Mamduhi, Dipankar Maity, Sandra Hirche +2
In the operation of networked control systems, where multiple processes share a resource-limited and time-varying cost-sensitive network, communication delay is inevitable and prim…
Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression
Wenxin Xiao, Armin Lederer, Sandra Hirche
Modelling real world systems involving humans such as biological processes for disease treatment or human behavior for robotic rehabilitation is a challenging problem because label…
Prediction with Approximated Gaussian Process Dynamical Models
Thomas Beckers, Sandra Hirche
The modeling and simulation of dynamical systems is a necessary step for many control approaches. Using classical, parameter-based techniques for modeling of modern systems, e.g.,…
How Training Data Impacts Performance in Learning-based Control
Armin Lederer, Alexandre Capone, Jonas Umlauft +1
When first principle models cannot be derived due to the complexity of the real system, data-driven methods allow us to build models from system observations. As these models are e…
Data selection for multi-task learning under dynamic constraints
Alexandre Capone, Armin Lederer, Jonas Umlauft +1
Learning-based techniques are increasingly effective at controlling complex systems using data-driven models. However, most work done so far has focused on learning individual task…
AoI-based Finite Horizon Scheduling for Heterogeneous Networked Control Systems
Onur Ayan, H. Murat Gürsu, Sandra Hirche +1
Age of information (AoI) measures information freshness at the receiver. AoI may provide insights into quality of service in communication systems. For this reason, it has been use…
Feedback Linearization based on Gaussian Processes with event-triggered Online Learning
Jonas Umlauft, Sandra Hirche
Combining control engineering with nonparametric modeling techniques from machine learning allows to control systems without analytic description using data-driven models. Most exi…