Felipe Arias

Arias is improving mobile robot navigation by developing algorithms that consider a robot’s surroundings, experience, and relation to other agents when moving in constrained pedestrian environments such as hospitals, schools, or homes. He recently published a paper outlining a method that enables groups of robots to compute paths and avoid collisions efficiently and a self-supervision
methodology for training data generation, which he developed with researchers from Google Brain. Originally from Bolivia, Arias immigrated to the United States when he was 12 and is passionate about supporting the education and needs of fellow immigrants and Hispanics interested in STEM careers.