Flightmare: A Flexible Quadrotor Simulator

Yunlong Song, Selim Naji, Elia Kaufmann, Antonio Loquercio, Davide Scaramuzza
Proceedings of the 2020 Conference on Robot Learning, PMLR 155:1147-1157, 2021.

Abstract

State-of-the-art quadrotor simulators have a rigid and highly-specialized structure: either are they really fast, physically accurate, or photo-realistic. In this work, we propose a paradigm shift in the development of simulators: moving the trade-off between accuracy and speed from the developers to the end-users. We use this idea to develop a flexible quadrotor simulator: Flightmare. In this work, we propose a novel quadrotor simulator: Flightmare. Flightmare is composed of two main components: a configurable rendering engine built on Unity and a flexible physics engine for dynamics simulation. Those two components are totally decoupled and can run independently of each other. This makes our simulator extremely fast: rendering achieves speeds of up to 230 Hz, while physics simulation of up to 200,000 Hz on a laptop. In addition, Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene; (ii) an API for reinforcement learning which can simulate hundreds of quadrotors in parallel; and (iii) integration with a virtual-reality headset for interaction with the simulated environment. We demonstrate the flexibility of Flightmare by using it for two different robotic tasks: quadrotor control using deep reinforcement learning and collision-free path planning in a complex 3D environment.

Cite this Paper

BibTeX
@InProceedings{pmlr-v155-song21a, title = {Flightmare: A Flexible Quadrotor Simulator}, author = {Song, Yunlong and Naji, Selim and Kaufmann, Elia and Loquercio, Antonio and Scaramuzza, Davide}, booktitle = {Proceedings of the 2020 Conference on Robot Learning}, pages = {1147--1157}, year = {2021}, editor = {Kober, Jens and Ramos, Fabio and Tomlin, Claire}, volume = {155}, series = {Proceedings of Machine Learning Research}, month = {16--18 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v155/song21a/song21a.pdf}, url = {https://proceedings.mlr.press/v155/song21a.html}, abstract = {State-of-the-art quadrotor simulators have a rigid and highly-specialized structure: either are they really fast, physically accurate, or photo-realistic. In this work, we propose a paradigm shift in the development of simulators: moving the trade-off between accuracy and speed from the developers to the end-users. We use this idea to develop a flexible quadrotor simulator: Flightmare. In this work, we propose a novel quadrotor simulator: Flightmare. Flightmare is composed of two main components: a configurable rendering engine built on Unity and a flexible physics engine for dynamics simulation. Those two components are totally decoupled and can run independently of each other. This makes our simulator extremely fast: rendering achieves speeds of up to 230 Hz, while physics simulation of up to 200,000 Hz on a laptop. In addition, Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene; (ii) an API for reinforcement learning which can simulate hundreds of quadrotors in parallel; and (iii) integration with a virtual-reality headset for interaction with the simulated environment. We demonstrate the flexibility of Flightmare by using it for two different robotic tasks: quadrotor control using deep reinforcement learning and collision-free path planning in a complex 3D environment.} }
Endnote
%0 Conference Paper %T Flightmare: A Flexible Quadrotor Simulator %A Yunlong Song %A Selim Naji %A Elia Kaufmann %A Antonio Loquercio %A Davide Scaramuzza %B Proceedings of the 2020 Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2021 %E Jens Kober %E Fabio Ramos %E Claire Tomlin %F pmlr-v155-song21a %I PMLR %P 1147--1157 %U https://proceedings.mlr.press/v155/song21a.html %V 155 %X State-of-the-art quadrotor simulators have a rigid and highly-specialized structure: either are they really fast, physically accurate, or photo-realistic. In this work, we propose a paradigm shift in the development of simulators: moving the trade-off between accuracy and speed from the developers to the end-users. We use this idea to develop a flexible quadrotor simulator: Flightmare. In this work, we propose a novel quadrotor simulator: Flightmare. Flightmare is composed of two main components: a configurable rendering engine built on Unity and a flexible physics engine for dynamics simulation. Those two components are totally decoupled and can run independently of each other. This makes our simulator extremely fast: rendering achieves speeds of up to 230 Hz, while physics simulation of up to 200,000 Hz on a laptop. In addition, Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene; (ii) an API for reinforcement learning which can simulate hundreds of quadrotors in parallel; and (iii) integration with a virtual-reality headset for interaction with the simulated environment. We demonstrate the flexibility of Flightmare by using it for two different robotic tasks: quadrotor control using deep reinforcement learning and collision-free path planning in a complex 3D environment.
APA
Song, Y., Naji, S., Kaufmann, E., Loquercio, A. & Scaramuzza, D.. (2021). Flightmare: A Flexible Quadrotor Simulator. Proceedings of the 2020 Conference on Robot Learning, in Proceedings of Machine Learning Research 155:1147-1157 Available from https://proceedings.mlr.press/v155/song21a.html.

Related Material