22k citations · 34.8k across the 45 of their papers we have counts for
10 papers · 1 filter
Unsupervised Learning of Shape and Pose with Differentiable Point Clouds
Eldar Insafutdinov, Alexey Dosovitskiy
We address the problem of learning accurate 3D shape and camera pose from a collection of unlabeled category-specific images. We train a convolutional network to predict both the s…
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…
On Offline Evaluation of Vision-based Driving Models
Felipe Codevilla, Antonio M. López, Vladlen Koltun +1
Autonomous driving models should ideally be evaluated by deploying them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vas…
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…
On Evaluation of Embodied Navigation Agents
Peter Anderson, Angel Chang, Devendra Singh Chaplot +8
Skillful mobile operation in three-dimensional environments is a primary topic of study in Artificial Intelligence. The past two years have seen a surge of creative work on navigat…
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun +1
Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using tabular representations and linear functi…
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…
Semi-parametric Topological Memory for Navigation
Nikolay Savinov, Alexey Dosovitskiy, Vladlen Koltun
We introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals. The proposed semi-parametric topological…
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Nikolaus Mayer, Eddy Ilg, Philipp Fischer +4
The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations. As a…