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Champion-level drone racing using deep reinforcement learning
Elia Kaufmann1, Leonard Bauersfeld2, Antonio Loquercio2
1Robotics and Perception Group, University of Zurich, Zürich, Switzerland. ekaufmann@ifi.uzh.ch.
Nature
|August 30, 2023
Summary
Swift, an autonomous system, achieved human world champion performance in drone racing by combining deep reinforcement learning with real-world data. This AI system won head-to-head races, demonstrating a new milestone for autonomous mobile robotics.
Area of Science:
- Robotics
- Artificial Intelligence
- Machine Learning
Background:
- First-person view (FPV) drone racing requires high-speed piloting and precise navigation.
- Autonomous drones face challenges in operating at physical limits using only onboard sensors.
Purpose of the Study:
- To develop an autonomous system capable of competing at the level of human world champions in FPV drone racing.
- To demonstrate the feasibility of advanced AI in high-speed, sensor-limited robotic navigation.
Main Methods:
- The Swift system integrates deep reinforcement learning (RL) trained in simulation.
- Real-world flight data was incorporated to enhance the RL model's performance.
- The autonomous system was tested in head-to-head races against professional human pilots.
Main Results:
- Swift demonstrated competitive performance against human world champions in real-world races.
- The autonomous system achieved the fastest recorded race time.
- Swift won multiple races against elite human competitors.
Conclusions:
- Swift represents a significant advancement in autonomous mobile robotics and machine intelligence.
- Hybrid learning approaches combining simulation and real-world data are effective for complex robotic tasks.
- This research paves the way for deploying advanced AI in other dynamic physical systems.
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