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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Learning Deep Sensorimotor Policies for Vision-Based Autonomous Drone Racing |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 978-1-6654-9190-7 |
| ISSN | 2153-0858 |
| Page Range | 5243 - 5250 |
| Event Title | 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023 |
| Event Type | conference |
| Event Location | Detroit, MI, United States of America |
| Event Start Date | October 1 - 2023 |
| Event End Date | October 5 - 2023 |
| Series Name | IEEE International Conference on Intelligent Robots and Systems. Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers |
| Abstract Text | 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 to train a deep sensorimotor policy for vision-based drone racing. This approach eliminates the need for globally-consistent state estimation, trajectory planning, and handcrafted control design, allowing the policy to directly infer control commands from raw images, similar to human pilots. We conduct experiments using a realistic simulator and show that our vision-based policy can achieve state-of-the-art racing performance while being robust against unseen visual disturbances. Our study suggests that consistent feature embeddings are essential for achieving robust control performance in the presence of visual disturbances. The key to acquiring consistent feature embeddings is utilizing contrastive learning along with data augmentation. |
| Digital Object Identifier | 10.1109/IROS55552.2023.10341805 |
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