Learning High-Speed Flight in the Wild

Abstract

Quadrotors are agile. Unlike most other machines, they can traverse extremely complex environments at high speeds. To date, only expert human pilots have been able to fully exploit their capabilities. Autonomous operation with onboard sensing and computation has been limited to low speeds. State-of-the-art methods generally separate the navigation problem into subtasks: sensing, mapping, and planning. Although this approach has proven successful at low speeds, the separation it builds upon can be problematic for high-speed navigation in cluttered environments. Here, we propose an end-to-end approach that can autonomously fly quadrotors through complex natural and human-made environments at high speeds with purely onboard sensing and computation. The key principle is to directly map noisy sensory observations to collision-free trajectories in a receding-horizon fashion. The sensorimotor mapping is performed by a convolutional network trained exclusively in simulation via privileged learning: imitating an expert with access to privileged information. By simulating realistic sensor noise, our approach achieves zero-shot transfer to challenging real-world environments never seen during training: dense forests, snow-covered terrain, derailed trains, and collapsed buildings.

Resources

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Citation

@article{loquercio2021learning,
  title     = {Learning High-Speed Flight in the Wild},
  author    = {Loquercio, Antonio and Kaufmann, Elia and Ranftl, Rene and M{\"{u}}ller, Matthias and Koltun, Vladlen and Scaramuzza, Davide},
  journal   = {Science Robotics},
  volume    = {6},
  number    = {59},
  year      = {2021},
  doi       = {10.1126/scirobotics.abg5810}
}