Bardienus P. Duisterhof
University of Virginia, Harvard University Press, Delft University of Technology, Harvard University, Carnegie Mellon University
Papers
9
Total Citations
124
H-Index
6
About
Bardienus P. Duisterhof is a robotics researcher whose work sits at the intersection of machine learning, micro aerial vehicles, and resource-constrained autonomy. His primary research areas include tiny robot learning, deep reinforcement learning for nano drones, and perception for manipulation, with a focus on enabling intelligent behavior on severely limited hardware. Duisterhof's most impactful contributions center on deploying deep reinforcement learning directly onboard nano quadcopters—demonstrating fully autonomous source seeking using only the microcontroller of a 27g drone, a breakthrough that earned his work over 80 combined citations. His influential 2022 survey on tiny robot learning (38 citations) systematically maps the challenges of running ML on resource-constrained robots, while his 2019 and 2021 papers show that deep-RL policies can be executed in real-time on these platforms. Beyond flight, Duisterhof has tackled transparent object manipulation using residual NeRFs and deformable object manipulation with scene flow estimation. His work on tailless flapping-wing MAVs performing monocular visual servoing further showcases his versatility. Through these achievements, Duisterhof is pushing the boundaries of what tiny, low-cost robots can accomplish autonomously.
Research Focus
Key Achievements
Top Papers
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- 2Tiny Robot Learning (tinyRL) for Source Seeking on a Nano Quadcopter26 citations · 2021
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- 4A Tailless Flapping Wing MAV Performing Monocular Visual Servoing Tasks12 citations · 2020
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