Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing

• URL: http://arxiv.org/abs/2210.14985v1
• Date: Wed, 26 Oct 2022 19:03:17 GMT
• Title: Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing
• Authors: Jiawei Fu, Yunlong Song, Yan Wu, Fisher Yu, Davide Scaramuzza
• Abstract summary: Existing systems often require hand-engineered components for state estimation, planning, and control. This paper tackles the vision-based autonomous-drone-racing problem by learning deep sensorimotor policies.
• Score: 52.50284630866713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous drones can operate in remote and unstructured environments, enabling various real-world applications. However, the lack of effective vision-based algorithms has been a stumbling block to achieving this goal. Existing systems often require hand-engineered components for state estimation, planning, and control. Such a sequential design involves laborious tuning, human heuristics, and compounding delays and errors. This paper tackles the vision-based autonomous-drone-racing problem by learning deep sensorimotor policies. We use contrastive learning to extract robust feature representations from the input images and leverage a two-stage learning-by-cheating framework for training a neural network policy. The resulting policy directly infers control commands with feature representations learned from raw images, forgoing the need for globally-consistent state estimation, trajectory planning, and handcrafted control design. Our experimental results indicate that our vision-based policy can achieve the same level of racing performance as the state-based policy while being robust against different visual disturbances and distractors. We believe this work serves as a stepping-stone toward developing intelligent vision-based autonomous systems that control the drone purely from image inputs, like human pilots.

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