I am a Ph.D. student at Carnegie Mellon University (CMU), in the Robotics Institute advised by Sebastian Scherer.
Before coming to CMU, I completed a Bachelor's and Master's degree in Aerospace Engineering at Delft University of Technology, in the Netherlands.
Advised by Guido de Croon, I studied efficient bio-inspired algorithms for autonomous flight of tiny flying drones.
In 2019 I was a visiting student at Vijay Janapa Reddi's Edge Computing lab, at Harvard University, where we studied Deep Reinforcement Learning for tiny robots.
My research focus continues to be on efficient AI for fully autonomous operation of resource-constrained miniature robots.
At the intersection of computer vision, machine learning, and systems, I hope to contribute to a future where robots are affordable, safe, and useful.
We have developed a swarm of autonomous, tiny drones that is able to localize gas sources in unknown, cluttered environments. Bio-inspired AI allows the drones to tackle this complex task without any external infrastructure.
We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable inference of a deep-RL policy onboard a nano quadcopter.
This paper describes the computer vision and control algorithms used to achieve autonomous flight with the ∼30g tailless flapping wing robot, used to participate in the International Micro Air Vehicle Conference and Competition (IMAV 2018) indoor microair vehicle competition.