The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Champion-level drone racing using deep reinforcement learning
Open accessAbstract
First-person view (FPV) drone racing is a televised sport in which professional competitors pilot high-speed aircraft through a 3D circuit. Each pilot sees the environment from the perspective of their drone by means of video streamed from an onboard camera. Reaching the level of professional pilots with an autonomous drone is challenging because the robot needs to fly at its physical limits while estimating its speed and location in the circuit exclusively from onboard sensors 1. Here we introduce Swift, an autonomous system that can race physical vehicles at the level of the human world champions. The system combines deep reinforcement learning (RL) in simulation with data collected in the physical world. Swift competed against three human champions, including the world champions of two international leagues, in real-world head-to-head races. Swift won several races against each of the human champions and demonstrated the fastest recorded race time. This work represents a milestone for mobile robotics and machine intelligence 2, which may inspire the deployment of hybrid learning-based solutions in other physical systems.
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Cites
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Human-level control through deep reinforcement learning
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Mastering the game of Go with deep neural networks and tree search
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Proximal Policy Optimization Algorithms
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Mastering the game of Go without human knowledge
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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562 · cited 1×
“r λ λ = + − (9) t t ω t t cmd 4 5 −1 2 a a a”
Grandmaster level in StarCraft II using multi-agent reinforcement learning
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Learning dexterous in-hand manipulation
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Self-citationMastering Atari, Go, chess and shogi by planning with a learned model
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Cited by
Loss of plasticity in deep continual learning
Dohare, S., Hernandez-Garcia, J. F., Lan, Q., Rahman, P., Mahmood, A. R., & Sutton, R. S. (2024) · Nature · doi:10.1038/s41586-024-07711-7 · Cited by 172
“Machine learning and artificial intelligence have made remarkable progress in the past decade, with landmark successes in natural-language processing 2,3 , biology 4 , game playing 5–8 and robotics 9,10 .”
Model-based reinforcement learning for ultrasound-driven autonomous microrobots
Medany, M., Piglia, L., Achenbach, L., Mukkavilli, S. K., & Ahmed, D. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01054-2 · Cited by 56
Guo, J., Zhou, Y., Burlion, L., Savkin, A. V., & Huang, C. (2025) · Control Engineering Practice · doi:10.1016/j.conengprac.2025.106491 · Cited by 13
Trumpp, R., Javanmardi, E., Nakazato, J., Tsukada, M., & Caccamo, M. (2024) · 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801657 · Cited by 6
RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms
Ghignone, E., Baumann, N., Hu, C., Wang, J., Xie, L., Carron, A., & Magno, M. (2025) · 2025 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra55743.2025.11127278 · Cited by 1
“RL offers a promising solution for racing setups, being able to achieve super-human performance in driving simulators [1], and similarly being able to reach the level of human professionals in the setting of drone racing [2].”
Metadata
- Authors
- Kaufmann, E., Bauersfeld, L., Loquercio, A., Müller, M., Koltun, V., & Scaramuzza, D.
- Venue
- Nature
- Published
- 2023-08-30
- Volume
- 620
- Issue
- 7976
- Pages
- 982-987
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4386285856
- Cited by
- 710
- References
- 39
- FWCI
- 62.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Kaufmann, E., Bauersfeld, L., Loquercio, A., Müller, M., Koltun, V., & Scaramuzza, D. (2023). Champion-level drone racing using deep reinforcement learning. Nature. https://doi.org/10.1038/s41586-023-06419-4
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
