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Author ORCID Identifier
https://orcid.org/0009-0000-8423-3466
Date Available
5-27-2026
Year of Publication
2026
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Engineering
Department/School/Program
Electrical and Computer Engineering
Faculty
YuMing Zhang
Faculty
Daniel Lau
Abstract
Arc welding processes demand real-time adaptive control that current robotic systems cannot achieve autonomously. This dissertation develops a systematic framework to robotize complex welding by learning from human demonstration, integrating generative modeling, physics-informed reconstruction, and model-based imitation learning. First, human--robot collaboration systems are established for both Gas Tungsten Arc Welding (GTAW) and Double-Electrode Gas Metal Arc Welding, combining robotic teleoperation with virtual reality interfaces to capture high-quality operator demonstrations. Second, a physics-informed neural network framework reconstructs complete molten pool flow fields from high-speed imaging, enriching process understanding beyond direct sensor observation. Third, generative models, including a hybrid latent variational autoencoder for predictive weld pool visualization and a dynamic variational autoencoder with particle filtering for robust state estimation, learn compact, temporally coherent representations of welding dynamics that enhance both human control and machine-interpretable process states. Fourth, model-based imitation learning, combining adversarial imitation with neural ordinary differential equation dynamics, extracts and refines human expertise for autonomous GTAW control. Fifth, a bidirectional multi-rate control architecture extends the paradigm to balance-critical humanoid platforms for precision manufacturing. Together, these contributions establish a transferable pipeline from human demonstration capture to autonomous robotic welding execution.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2026.19
Archival?
Archival
Funding Information
This research was supported by:
- National Science Foundation, "Optimal Transport Generative Adversarial Networks: Theory, Algorithms, and Applications" (Award No. 2327113), 2023–2026.
- National Science Foundation, "Intelligent Co-robots for Complex Welding Manufacturing through Learning and Generalization of Welders' Capabilities" (Award No. 2024614), 2020–2025.
Recommended Citation
Cao, Yue, "Robotizing Complex Welding Processes Through Imitation Learning and Generative Models from Human Demonstration" (2026). Theses and Dissertations--Electrical and Computer Engineering. 229.
https://uknowledge.uky.edu/ece_etds/229
