Papers
77
Total Citations
2,986
H-Index
25
About
Maurice Fallon is a leading robotics researcher whose work sits at the intersection of state estimation, legged locomotion, and autonomous navigation. Based at the University of Oxford, Fallon has made foundational contributions to how robots perceive and move through complex, real-world environments — from underground tunnels to uneven outdoor terrain. His most cited work, "Optimization-based locomotion planning, estimation, and control design for the Atlas humanoid robot" (2015, 814 citations), helped define the architecture for capable humanoid systems and grew directly from participation in the landmark DARPA Robotics Challenge. This challenge also inspired influential work on whole-body planning and affordance-based perception. Fallon has pioneered tightly-coupled multi-sensor fusion, developing systems like VILENS and unified lidar-visual-inertial odometry frameworks that allow legged robots to navigate reliably where individual sensors fail. His Pronto state estimator has become a widely adopted platform for real-world legged robot deployment. Fallon's research on probabilistic contact estimation and drift-free humanoid localization has addressed core challenges in making legged robots practically trustworthy. His team's involvement in the DARPA Subterranean Challenge further demonstrated these methods at scale in demanding autonomous exploration scenarios. With thousands of cumulative citations, Fallon's work has profoundly shaped modern legged robotics research.
Research Focus
Key Achievements
Top Papers
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- 2VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots160 citations · 2022
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- 4An Architecture for Online Affordance‐based Perception and Whole‐body Planning140 citations · 2014
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