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Link to original content: https://doi.org/10.48550/arXiv.2304.04128
[2304.04128] Learning Agile, Vision-based Drone Flight: from Simulation to Reality
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Computer Science > Robotics

arXiv:2304.04128 (cs)
[Submitted on 9 Apr 2023]

Title:Learning Agile, Vision-based Drone Flight: from Simulation to Reality

Authors:Davide Scaramuzza, Elia Kaufmann
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Abstract:We present our latest research in learning deep sensorimotor policies for agile, vision-based quadrotor flight. We show methodologies for the successful transfer of such policies from simulation to the real world. In addition, we discuss the open research questions that still need to be answered to improve the agility and robustness of autonomous drones toward human-pilot performance.
Subjects: Robotics (cs.RO)
Cite as: arXiv:2304.04128 [cs.RO]
  (or arXiv:2304.04128v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2304.04128

Submission history

From: Elia Kaufmann [view email]
[v1] Sun, 9 Apr 2023 00:34:42 UTC (7,114 KB)
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