Deep Drone Acrobatics (Extended Abstract)

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

doi:10.24963/ijcai.2021/650

Deep Drone Acrobatics (Extended Abstract)

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

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Sister Conferences Best Papers. Pages 4780-4783.

https://doi.org/10.24963/ijcai.2021/650

Keywords

Robotics: Learning in Robotics
Machine Learning Applications: Applications of Supervised Learning
Robotics: Behavior and Control

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.

Elia Kaufmann, Antonio Loquercio, Rene Ranftl, Matthias Müller, Vladlen Koltun, and Davide Scaramuzza. 2021. Deep Drone Acrobatics (Extended Abstract). In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4780–4783. https://doi.org/10.24963/ijcai.2021/650

@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    = {Prof. 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},
}

TY  - CPAPER
TI  - Deep Drone Acrobatics (Extended Abstract)
AU  - Kaufmann, Elia
AU  - Loquercio, Antonio
AU  - Ranftl, Rene
AU  - Müller, Matthias
AU  - Koltun, Vladlen
AU  - Scaramuzza, Davide
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4780
EP  - 4783
DO  - 10.24963/ijcai.2021/650
UR  - https://doi.org/10.24963/ijcai.2021/650
ER  -