Champion-level drone racing using deep reinforcement learning
Problem
Autonomous drones could not match professional human pilots in high-speed first-person-view racing using only onboard sensing.
Why it matters
Matching champion pilots at physical-limit flight is a hard benchmark for autonomous robotics.
What the AI did
Swift combined deep reinforcement learning trained in simulation with real-world data to race physical drones autonomously.
Results
Won several races against each of three human champions, including two world champions, and demonstrated the fastest recorded race time.
Impact
First stated autonomous system to beat human world champions in real-world head-to-head drone races.
Record
| Venue | Nature |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Elia Kaufmann, Leonard Bauersfeld, Antonio Loquercio, Matthias Müller, Vladlen Koltun, Davide Scaramuzza |
| Citations | 709 (OpenAlex, at import time) |
| References | 53 |
| Model or method | Swift |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1038/s41586-023-06419-4 |
| Open access | hybrid |
| Keywords | drone racing, deep reinforcement learning, autonomous flight, FPV, Swift, robotics |
Importance rating
real but narrower advance
Abstract calls it a milestone for mobile robotics and machine intelligence, beating world champions with fastest recorded race time; a hard benchmark and new capability, but the racing domain itself is narrow.
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