activity
20182021
most citedLearning High-Speed Flight in the Wild

351 citations · 351 across the 1 of their papers we have counts for

collaborators

10 papers

cs.RO2021351 cited

Learning High-Speed Flight in the Wild

Antonio Loquercio, Elia Kaufmann, René Ranftl +3

Quadrotors are agile. Unlike most other machines, they can traverse extremely complex environments at high speeds. To date, only expert human pilots have been able to fully exploit…

cs.RO2020

OpenBot: Turning Smartphones into Robots

Matthias Müller, Vladlen Koltun

Current robots are either expensive or make significant compromises on sensory richness, computational power, and communication capabilities. We propose to leverage smartphones to…

cs.RO2020

Deep Drone Acrobatics

Elia Kaufmann, Antonio Loquercio, René Ranftl +3

Performing acrobatic maneuvers with quadrotors is extremely challenging. Acrobatic flight requires high thrust and extreme angular accelerations that push the platform to its physi…

cs.LG2019

SGAS: Sequential Greedy Architecture Search

Guohao Li, Guocheng Qian, Itzel C. Delgadillo +3

Architecture design has become a crucial component of successful deep learning. Recent progress in automatic neural architecture search (NAS) shows a lot of promise. However, disco…

cs.RO2019

Learning a Controller Fusion Network by Online Trajectory Filtering for Vision-based UAV Racing

Matthias Müller, Guohao Li, Vincent Casser +3

Autonomous UAV racing has recently emerged as an interesting research problem. The dream is to beat humans in this new fast-paced sport. A common approach is to learn an end-to-end…

cs.CV2019

DeepGCNs: Can GCNs Go as Deep as CNNs?

Guohao Li, Matthias Müller, Ali Thabet +1

Convolutional Neural Networks (CNNs) achieve impressive performance in a wide variety of fields. Their success benefited from a massive boost when very deep CNN models were able to…