Pasquale Antonante
Decision Systems (United States), Massachusetts Institute of Technology
Papers
8
Total Citations
364
H-Index
5
About
Pasquale Antonante is a robotics and autonomous systems researcher whose work sits at the intersection of robust estimation, spatial perception, and safety-critical autonomy. He is best known for his landmark 2020 paper "Graduated Non-Convexity for Robust Spatial Perception," which has amassed over 300 citations and introduced a powerful framework combining Semidefinite Programming and Sums-of-Squares relaxations to achieve certifiably optimal, outlier-robust solutions for core robotics and computer vision problems. This contribution fundamentally advanced how autonomous systems handle noisy, corrupted sensor data in real-world environments. Antonante has also made significant strides in formalizing outlier-robust estimation more broadly, developing minimally tuned algorithms with theoretical guarantees applicable across localization, mapping, and pose estimation tasks. A complementary thread of his research addresses the runtime monitoring and fault detection of perception systems — a critical concern for high-integrity applications like self-driving vehicles where perception failures can have life-threatening consequences. Beyond research, Antonante has contributed to robotics education, helping develop and open-source MIT's "Visual Navigation for Autonomous Vehicles" graduate course. His body of work reflects a principled commitment to making autonomous perception both mathematically rigorous and deployable in safety-critical settings.
Research Focus
Key Achievements
Top Papers
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- 4Monitoring and Diagnosability of Perception Systems7 citations · 2021
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