Aleksandra Faust
Google (United States), University of New Mexico, Sandia National Laboratories
Papers
39
Total Citations
1,266
H-Index
16
About
Aleksandra Faust is a pioneering robotics and machine learning researcher whose work sits at the intersection of reinforcement learning, autonomous navigation, and aerial robotics. She is best known for developing innovative methods that enable robots to learn complex behaviors in challenging, real-world conditions — from ground-based navigation to aerial cargo delivery. Faust's early contributions focused on UAVs carrying suspended loads, where she applied reinforcement learning to generate swing-free trajectories and autonomous manipulation behaviors, work that garnered over 380 combined citations and laid important groundwork for practical aerial robotics. Her 2019 paper on AutoRL, which trains end-to-end navigation policies using evolutionary automation, has become a landmark reference in robot learning with 230 citations, demonstrating how robots can navigate dynamic environments using raw sensor data alone. Beyond navigation, Faust has made significant contributions to safe reinforcement learning, formulating safety constraints through Lyapunov-based policy optimization (153 citations), and to natural language-guided robot navigation through her FollowNet architecture. Her more recent work addresses the emerging challenge of deploying machine learning on resource-constrained robots — including tiny robots and aerial platforms — through benchmarking tools like Air Learning. Across her career, Faust has consistently bridged theoretical rigor with deployable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Learning Navigation Behaviors End-to-End With AutoRL230 citations · 2019
- 2Automated aerial suspended cargo delivery through reinforcement learning166 citations · 2014
- 3Lyapunov-based Safe Policy Optimization for Continuous Control153 citations · 2019
- 4Learning swing-free trajectories for UAVs with a suspended load134 citations · 2013
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