Max Robotics — Global Service Robot Directory + US Certification

About

Tudor Jianu is a robotics and medical AI researcher whose work spans two high-impact domains: tactile sensing for robotic manipulation and autonomous endovascular intervention. His early research tackled the persistent sim-to-real gap in optical tactile sensing, developing deep texture generation networks and unsupervised adversarial domain adaptation techniques that enable robots to transfer tactile skills learned in simulation directly to real-world environments — work that has collectively garnered over 40 citations and represents a meaningful advance in reducing costly real-world data collection. Jianu's contributions to medical robotics are equally significant; his development of CathSim, an open-source simulator for endovascular intervention, has already attracted 15 citations since 2024 and provides the research community with a much-needed accessible platform for training autonomous catheterization systems safely and efficiently. His more recent work on federated learning for endovascular foundation models signals a growing interest in privacy-preserving, data-efficient AI for surgical applications. Across these research threads, Jianu consistently addresses a common challenge — bridging the gap between controlled training environments and complex real-world deployment — making his work particularly valuable for students and researchers working at the intersection of robotics, simulation, and clinical AI.