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About

Laurent Jenni is a robotics researcher whose work bridges precision engineering and neuroscience, with a focus on micro-robotic systems and human-robot interaction. His primary research areas include adaptive control for parallel kinematic manipulators (PKMs), collision avoidance for densely packed robotic systems, and the development of miniature fiber positioners for astrophysical spectroscopy. Jenni’s most notable contribution is the design of a novel adaptive control scheme based on RISE (Robust Integral of the Sign of the Error) theory for PKMs, which enhances stability and performance without relying on full dynamic models—a paper that has garnered 38 citations. He also played a key role in advancing the Dark Energy Spectroscopic Instrument (DESI) project, developing micro DC-brushless motor drivers and collision-free motion planning algorithms for thousands of pen-sized fiber positioner robots, enabling massive spectroscopic surveys to probe dark energy and galactic formation. His work on collision avoidance, cited 31 times, and an 8-mm diameter fiber robot positioner (19 citations) have been instrumental in next-generation survey spectrographs. In a striking interdisciplinary leap, Jenni has also explored neuroscience robotics, designing a system for the controlled induction and real-time assessment of hallucinations (18 citations). His achievements demonstrate a rare ability to solve complex engineering challenges at micro-scales while pushing into cognitive robotics.