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About

Milad Jami is a robotics researcher whose work lies at the intersection of industrial automation and human-aware mobile robot perception. His key research areas include offline programming for assembly, 3D perception, and multi-task learning for autonomous systems. Jami’s most notable contribution is the development of an automated trajectory generator for assembly processes that requires only sparse manual input, significantly reducing the programming burden for complex tasks like part assembly with tight fittings—a paper that has garnered 4 citations. He has also advanced mobile robot reliability by bridging depth estimation and completion for robust 3D perception, and pioneered efficient human 3D localization combined with free space segmentation, enabling socially-aware navigation in warehouse environments. His work on multi-task learning, using a simulated warehouse dataset, demonstrates a scalable approach to processing multiple perception tasks simultaneously, enhancing robot situational awareness. With papers accumulating citations in the single digits, Jami’s research is foundational for integrating robots into shared human spaces, promising safer, more intuitive industrial automation.