Deep Drone Acrobatics (Extended Abstract)

Elia Kaufmann; Antonio Loquercio; Rene Ranftl; Matthias Müller; Vladlen Koltun; Davide Scaramuzza

IJCAI 2021poster0 citations

Deep Drone Acrobatics (Extended Abstract)

Elia Kaufmann, Antonio Loquercio, Rene Ranftl, Matthias Müller, Vladlen Koltun, Davide Scaramuzza

Abstract

Acrobatic flight with quadrotors is extremely challenging. Maneuvers such as the loop, matty flip, or barrel roll require high thrust and extreme angular accelerations that push the platform to its limits. Human drone pilots require years of practice to safely master such maneuvers. Yet, a tiny mistake could make the platform lose control, and brutally crash. This short paper describes an approach to safely train acrobatic controllers in simulation and deploy them with no fine-tuning zero-shot transfer on physical quadrotors. The approach uses only onboard sensing and computation.

Robotics: Learning in RoboticsRobotics: Behavior and ControlMachine Learning Applications: Applications of Supervised Learning

BibTeX

@inproceedings{ijcai2021p650,
  title     = {Deep Drone Acrobatics (Extended Abstract)},
  author    = {Kaufmann, Elia and Loquercio, Antonio and Ranftl, Rene and Müller, Matthias and Koltun, Vladlen and Scaramuzza, Davide},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Zhi-Hua Zhou},
  pages     = {4780--4783},
  year      = {2021},
  month     = {8},
  note      = {Sister Conferences Best Papers},
  doi       = {10.24963/ijcai.2021/650},
  url       = {https://doi.org/10.24963/ijcai.2021/650},
}