8 papers
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…
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…