3 citations · 7 across the 26 of their papers we have counts for
26 papers
Event-triggered Control and Online Learning for Networked Systems under Computational Delays
Xiaobing Dai, Armin Lederer, Zewen Yang +4
Online learning-based control is a promising approach to control uncertain systems, where unknown components are identified during operation to improve control performance. However…
Asynchronous Cooperative Online Learning for Multi-Robot Control under Computational Delays
Xiaobing Dai, Zewen Yang, Wei Ren +1
Ensuring the safe operation of multi-agent systems (MASs) under uncertain environments is crucial for cooperative robotic, where external disturbances and inaccurate dynamic models…
Where to Put Safety? Control Barrier Function Placement in Networked Control Systems
Severin Beger, Yuling Chen, Sandra Hirche
Control barrier functions (CBFs) are widely used to enforce safety in autonomous systems, yet their placement within networked control architectures remains largely unexplored. In…
Online Bayesian Learning of Agent Behavior in Differential Games
Francesco Bianchin, Robert Lefringhausen, Sandra Hirche
This work introduces an online Bayesian game-theoretic method for behavior identification in multi-agent dynamical systems. By casting Hamilton-Jacobi-Bellman optimality conditions…
Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Control
Robert Lefringhausen, Theodor Springer, Sandra Hirche
Reliable optimal control is challenging when the dynamics of a nonlinear system are unknown and only infrequent, noisy output measurements are available. This work addresses this s…
HumanHalo: Safe and Efficient 3D Navigation Among Humans via Minimally Conservative MPC
Simon Schaefer, Helen Oleynikova, Sandra Hirche +1
Safe and efficient robotic navigation among humans is essential for integrating robots into everyday environments. Most existing approaches focus on simplified 2D crowd navigation…
Learning Safe Control via On-the-Fly Bandit Exploration
Alexandre Capone, Ryan Cosner, Aaaron Ames +1
Control tasks with safety requirements under high levels of model uncertainty are increasingly common. Machine learning techniques are frequently used to address such tasks, typica…
UniConFlow: A Unified Constrained Flow-Matching Framework for Certified Motion Planning
Zewen Yang, Xiaobing Dai, Dian Yu +4
Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation across various tasks. Yet, most existi…
Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks
Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche
The practical deployment of learning-based autonomous systems would greatly benefit from tools that flexibly obtain safety guarantees in the form of certificate functions from data…
SafeFlow: Safe Robot Motion Planning with Flow Matching via Control Barrier Functions
Xiaobing Dai, Zewen Yang, Dian Yu +4
Recent advances in generative modeling have led to promising results in robot motion planning, particularly through diffusion and flow matching (FM)-based models that capture compl…
A Set-Theoretic Robust Control Approach for Linear Quadratic Games with Unknown Counterparts
Francesco Bianchin, Robert Lefringhausen, Elisa Gaetan +2
Ensuring robust decision-making in multi-agent systems is challenging when agents have distinct, possibly conflicting objectives and lack full knowledge of each other's strategies.…
Barrier Certificates for Unknown Systems with Latent States and Polynomial Dynamics using Bayesian Inference
Robert Lefringhausen, Sami Leon Noel Aziz Hanna, Elias August +1
Certifying safety in dynamical systems is crucial, but barrier certificates - widely used to verify that system trajectories remain within a safe region - typically require explici…
Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document
Zewen Yang, Xiaobing Dai, Sandra Hirche
This is a complementary document for the paper titled "Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems".
Learning-based Parameterized Barrier Function for Safety-Critical Control of Unknown Systems
Sihua Zhang, Di-Hua Zhai, Xiaobing Dai +3
With the increasing complexity of real-world systems and varying environmental uncertainties, it is difficult to build an accurate dynamic model, which poses challenges especially…
Safe Event-triggered Gaussian Process Learning for Barrier-Constrained Control
Armin Lederer, Azra Begzadić, Sandra Hirche +2
While control barrier functions (CBFs) are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical lim…
Exponentially Stable Projector-based Control of Lagrangian Systems with Gaussian Processes
Giulio Evangelisti, Cosimo Della Santina, Sandra Hirche
Designing accurate yet robust tracking controllers with tight performance guarantees for Lagrangian systems is challenging due to nonlinear modeling uncertainties and conservative…
Computation-Aware Learning for Stable Control with Gaussian Process
Wenhan Cao, Alexandre Capone, Rishabh Yadav +2
In Gaussian Process (GP) dynamical model learning for robot control, particularly for systems constrained by computational resources like small quadrotors equipped with low-end pro…
Stable Inverse Reinforcement Learning: Policies from Control Lyapunov Landscapes
Samuel Tesfazgi, Leonhard Sprandl, Armin Lederer +1
Learning from expert demonstrations to flexibly program an autonomous system with complex behaviors or to predict an agent's behavior is a powerful tool, especially in collaborativ…
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes
Samuel Tesfazgi, Markus Keßler, Emilio Trigili +2
Ensuring safety and adapting to the user's behavior are of paramount importance in physical human-robot interaction. Thus, incorporating elastic actuators in the robot's mechanical…
Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions
Tzu-Yuan Huang, Sihua Zhang, Xiaobing Dai +4
In many control system applications, state constraint satisfaction needs to be guaranteed within a prescribed time. While this issue has been partially addressed for systems with k…
Analyzing the Impact of Computation in Adaptive Dynamic Programming for Stochastic LQR Problem
Wenhan Cao, Alexandre Capone, Sandra Hirche +1
Adaptive dynamic programming (ADP) for stochastic linear quadratic regulation (LQR) demands the precise computation of stochastic integrals during policy iteration (PI). In a fully…
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression
Xiaobing Dai, Zewen Yang, Mengtian Xu +3
Consensus control in multi-agent systems has received significant attention and practical implementation across various domains. However, managing consensus control under unknown d…
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies
Zewen Yang, Songbo Dong, Armin Lederer +5
This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating unde…
Whom to Trust? Elective Learning for Distributed Gaussian Process Regression
Zewen Yang, Xiaobing Dai, Akshat Dubey +2
This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs). The key contribution…