Search Results for author: Matthias Müller

Found 16 papers, 10 papers with code

Learning High-Speed Flight in the Wild

1 code implementation11 Oct 2021 Antonio Loquercio, Elia Kaufmann, René Ranftl, Matthias Müller, Vladlen Koltun, Davide Scaramuzza

Indeed, the subtasks are executed sequentially, leading to increased processing latency and a compounding of errors through the pipeline.

Jointly Learning Identification and Control for Few-Shot Policy Adaptation

no code implementations29 Sep 2021 Nina Wiedemann, Antonio Loquercio, Matthias Müller, Rene Ranftl, Davide Scaramuzza

We evaluate our approach on several complex systems and tasks, and experimentally analyze the advantages over model-free and model-based methods in terms of performance and sample efficiency.

Training Graph Neural Networks with 1000 Layers

4 code implementations14 Jun 2021 Guohao Li, Matthias Müller, Bernard Ghanem, Vladlen Koltun

Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges.

Graph Sampling Node Property Prediction

OpenBot: Turning Smartphones into Robots

1 code implementation24 Aug 2020 Matthias Müller, Vladlen Koltun

We develop a software stack that allows smartphones to use this body for mobile operation and demonstrate that the system is sufficiently powerful to support advanced robotics workloads such as person following and real-time autonomous navigation in unstructured environments.

Autonomous Navigation

Deep Drone Acrobatics

1 code implementation10 Jun 2020 Elia Kaufmann, Antonio Loquercio, René Ranftl, Matthias Müller, Vladlen Koltun, Davide Scaramuzza

In this paper, we propose to learn a sensorimotor policy that enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation.

Robotics

DeepGCNs: Making GCNs Go as Deep as CNNs

4 code implementations15 Oct 2019 Guohao Li, Matthias Müller, Guocheng Qian, Itzel C. Delgadillo, Abdulellah Abualshour, Ali Thabet, Bernard Ghanem

This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.

3D Point Cloud Classification 3D Semantic Segmentation +1

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

no code implementations18 Apr 2019 Matthias Müller, Guohao Li, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem

A common approach is to learn an end-to-end policy that directly predicts controls from raw images by imitating an expert.

DeepGCNs: Can GCNs Go as Deep as CNNs?

1 code implementation ICCV 2019 Guohao Li, Matthias Müller, Ali Thabet, Bernard Ghanem

Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3. 7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation.

3D Semantic Segmentation Graph Classification +1

SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

1 code implementation5 Dec 2018 Abdullah Hamdi, Matthias Müller, Bernard Ghanem

In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks.

Adversarial Attack Autonomous Driving +3

Driving Policy Transfer via Modularity and Abstraction

no code implementations25 Apr 2018 Matthias Müller, Alexey Dosovitskiy, Bernard Ghanem, Vladlen Koltun

Simulation can help end-to-end driving systems by providing a cheap, safe, and diverse training environment.

Autonomous Driving

OIL: Observational Imitation Learning

no code implementations3 Mar 2018 Guohao Li, Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem

Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-to-end policy, which directly predicts controls from raw images.

Autonomous Driving Autonomous Navigation +2

Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications

no code implementations19 Aug 2017 Matthias Müller, Vincent Casser, Jean Lahoud, Neil Smith, Bernard Ghanem

We present a photo-realistic training and evaluation simulator (Sim4CV) with extensive applications across various fields of computer vision.

Autonomous Driving

Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation

no code implementations19 Aug 2017 Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem

Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years.

Data Augmentation Imitation Learning

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