3 citations · 13 across the 21 of their papers we have counts for
21 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…
Quality or Quantity? Error-Informed Selective Online Learning with Gaussian Processes in Multi-Agent Systems: Extended Version
Zewen Yang, Xiaobing Dai, Jiajun Cheng +2
Effective cooperation is pivotal in distributed learning for multi-agent systems, where the interplay between the quantity and quality of the machine learning models is crucial. Th…
SAD-Flower: Flow Matching for Safe, Admissible, and Dynamically Consistent Planning
Tzu-Yuan Huang, Armin Lederer, Dai-Jie Wu +6
Flow matching (FM) has shown promising results in data-driven planning. However, it inherently lacks formal guarantees for ensuring state and action constraints, whose satisfaction…
Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learning
Zewen Yang, Xiaobing Dai, Dongfa Zhang +5
Learning from demonstration allows robots to acquire complex skills from human demonstrations, but conventional approaches often require large datasets and fail to generalize acros…
Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Version
Zewen Yang, Dongfa Zhang, Xiaobing Dai +5
Gaussian Processes (GPs), as a nonparametric learning method, offer flexible modeling capabilities and calibrated uncertainty quantification for function approximations. Additional…
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…
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…
GPgym: A Remote Service Platform with Gaussian Process Regression for Online Learning
Xiaobing Dai, Zewen Yang
Machine learning is now widely applied across various domains, including industry, engineering, and research. While numerous mature machine learning models have been open-sourced o…
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…
Kernel-based Learning for Safe Control of Discrete-Time Unknown Systems under Incomplete Observations
Zewen Yang, Xiaobing Dai, Weijie Yang +3
Safe control for dynamical systems is critical, yet the presence of unknown dynamics poses significant challenges. In this paper, we present a learning-based control approach for t…
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…
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…
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy
Zhenxiao Yin, Xiaobing Dai, Zewen Yang +3
The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanen…
Semi-automated Thermal Envelope Model Setup for Adaptive Model Predictive Control with Event-triggered System Identification
Lu Wan, Xiaobing Dai, Torsten Welfonder +2
To reach carbon neutrality in the middle of this century, smart controls for building energy systems are urgently required. Model predictive control (MPC) demonstrates great potent…
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.…
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…