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About

Mauro Massari is a leading figure in space robotics and autonomous guidance, whose work bridges artificial intelligence, control theory, and planetary exploration. His most impactful contribution, the 2020 paper “Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach,” has garnered 104 citations, establishing a new paradigm for intelligent, adaptive landing systems. Massari’s research spans two major frontiers: the development of legged rovers for rough-terrain exploration—exemplified by his early work on a six-legged prototype controlled by evolved recurrent neural networks—and the precise control of free-flying orbital robots. In the latter domain, he has made seminal advances in contact detection, estimation, and reaction for space manipulators, including a nonlinear observer for unexpected collisions and a generalized force observer for contact wrench estimation. His 2024 work on combined satellite and robotic arm control for close-proximity operations further demonstrates his leadership in autonomous GNC systems. With a career that moves from planetary rovers to orbital robotics, Massari’s research is defined by its practical, testbed-validated innovations and its integration of learning-based methods with classical control, making him a pivotal contributor to the future of autonomous space exploration.