Robot Anomaly Detection

I researched on machine learning algorithms for robot health monitoring and manufacturing quality inspection, including detection of harmonic-drive failures, bearing wear, lubricant leakage, and surface defects in robot-manufactured products. I designed anomaly detection methods using image-patch representations, encoder-decoder networks, vision foundation models, learned feature weighting, Gaussian Mixture Models, and autoregressive time-series modeling.