Vision-based Control of a Quadrotor in User Proximity: Mediated vs End-to-End Learning Approaches

Dario Mantegazza; Jérôme Guzzi; Luca M. Gambardella; Alessandro Giusti

ICRA 2019poster17 citations

Vision-based Control of a Quadrotor in User Proximity: Mediated vs End-to-End Learning Approaches

Dario Mantegazza, Jérôme Guzzi, Luca M. Gambardella, Alessandro Giusti

Abstract

We consider the task of controlling a quadrotor to hover in front of a freely moving user, using input data from an onboard camera. On this specific task we compare two widespread learning paradigms: a mediated approach, which learns a high-level state from the input and then uses it for deriving control signals; and an end-to-end approach, which skips high-level state estimation altogether. We show that despite their fundamental difference, both approaches yield equivalent performance on this task. We finally qualitatively analyze the behavior of a quadrotor implementing such approaches.

BibTeX

@inproceedings{icra2019_visionbasedcontr,
  title = {Vision-based Control of a Quadrotor in User Proximity: Mediated vs End-to-End Learning Approaches},
  author = {Dario Mantegazza and Jérôme Guzzi and Luca M. Gambardella and Alessandro Giusti},
  booktitle = {ICRA 2019},
  year = {2019}
}