Publication: Learning Deep Sensorimotor Policies for Vision-Based Autonomous Drone Racing
Learning Deep Sensorimotor Policies for Vision-Based Autonomous Drone Racing
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Fu, J., Song, Y., Wu, Y., Yu, F., & Scaramuzza, D. (2023). Learning Deep Sensorimotor Policies for Vision-Based Autonomous Drone Racing. IEEE International Conference on Intelligent Robots and Systems. Proceedings, 5243–5250. https://doi.org/10.1109/IROS55552.2023.10341805
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The development of effective vision-based algorithms has been a significant challenge in achieving autonomous drones, which promise to offer immense potential for many real-world applications. This paper investigates learning deep sensorimotor policies for vision-based drone racing, which is a particularly demanding setting for testing the limits of an algorithm. Our method combines feature representation learning to extract task-relevant feature representations from high-dimensional image inputs with a learning-by-cheating framework
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Creators (Authors)
- Fu, Jiawei
- Song, Yunlong
- Wu, Yan
- Yu, Fisher
- Scaramuzza, Davide
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Fu, J., Song, Y., Wu, Y., Yu, F., & Scaramuzza, D. (2023). Learning Deep Sensorimotor Policies for Vision-Based Autonomous Drone Racing. IEEE International Conference on Intelligent Robots and Systems. Proceedings, 5243–5250. https://doi.org/10.1109/IROS55552.2023.10341805
