22k citations · 34.8k across the 45 of their papers we have counts for
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Deep Drone Racing: From Simulation to Reality with Domain Randomization
Antonio Loquercio, Elia Kaufmann, René Ranftl +3
Dynamically changing environments, unreliable state estimation, and operation under severe resource constraints are fundamental challenges that limit the deployment of small autono…
Learning agile and dynamic motor skills for legged robots
Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy +4
Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compellin…
Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing
Elia Kaufmann, Mathias Gehrig, Philipp Foehn +4
Autonomous micro aerial vehicles still struggle with fast and agile maneuvers, dynamic environments, imperfect sensing, and state estimation drift. Autonomous drone racing brings t…
Frequency-Aware Model Predictive Control
Ruben Grandia, Farbod Farshidian, Alexey Dosovitskiy +2
Transferring solutions found by trajectory optimization to robotic hardware remains a challenging task. When the optimization fully exploits the provided model to perform dynamic t…
Deep Drone Racing: Learning Agile Flight in Dynamic Environments
Elia Kaufmann, Antonio Loquercio, Rene Ranftl +3
Autonomous agile flight brings up fundamental challenges in robotics, such as coping with unreliable state estimation, reacting optimally to dynamically changing environments, and…
Driving Policy Transfer via Modularity and Abstraction
Matthias Müller, Alexey Dosovitskiy, Bernard Ghanem +1
End-to-end approaches to autonomous driving have high sample complexity and are difficult to scale to realistic urban driving. Simulation can help end-to-end driving systems by pro…
End-to-end Driving via Conditional Imitation Learning
Felipe Codevilla, Matthias Müller, Antonio López +2
Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be contro…