Matthew Brown

University of California, Berkeley, Microsoft Research (United Kingdom)

Papers

Total Citations

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About

Matthew Brown’s research lies at the intersection of robotics, control theory, and autonomous systems, with a particular focus on dynamic locomotion and aerial maneuvering. His most influential work introduces a nonlinear feedback controller for a tailed robot capable of aerial self-righting—a biologically inspired approach that uses only two degrees of actuation to control attitude during free fall. By deriving a simplified angular momentum expression and inverting it to compute shape velocities, Brown provided a computationally efficient solution for rapid reorientation, a critical capability for rescue or exploration robots operating in unstructured environments. This paper has garnered 59 citations, reflecting its foundational impact on tailed robot dynamics and bio-inspired control. More recently, Brown contributed to the AirSim Drone Racing Lab (2020), a high-fidelity simulation framework designed to accelerate research in autonomous drone racing. This platform integrates computer vision, planning, state estimation, and control, enabling rapid prototyping and machine learning experimentation. Though newer, it has already earned 14 citations and is poised to become a key resource for the growing field of autonomous aerial competition. Brown’s work exemplifies how theoretical control insights can translate into practical simulation tools, advancing both fundamental robotics and applied autonomy.