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.
During this time my work on bio-inspired intelligence with Guido de Croon also helped me receive the titel of best graduate in engineering of TU Delft in the academic year 2020-2021.
My research focus continues to be on scalable AI for fully autonomous operation of robots that interact with their environment.
At the intersection of computer vision, machine learning, and systems, I hope to contribute to a future where complex robotic automation is scalable, safe and useful.
In this work we present our methodology for accurate wide-angle calibration. Our pipeline generates an intermediate model, and leverages it to iteratively improve feature detection and eventually the camera parameters.
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.