Andreas Eitel
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View article: Concepts for Data Sovereignty in Digital Value Chains: Data Cockpits—Data Usage Control—Data Trustees
Concepts for Data Sovereignty in Digital Value Chains: Data Cockpits—Data Usage Control—Data Trustees Open
Digital value chains require the exchange of data. This data is always sensitive in one way or another—whether due to data protection, trade secret protection, or the very individual protection needs of data providers and data consumers al…
View article: Usage Control in the International Data Spaces
Usage Control in the International Data Spaces Open
In this report, we focus on data usage control and data provenance that are conceptual and technological solutions to cope with data sovereignty challenges. We introduce a common scenario for the Industry 4.0 age, in which a supplier and a…
View article: Usage Control in the International Data Spaces
Usage Control in the International Data Spaces Open
In this report, we focus on data usage control and data provenance that are conceptual and technological solutions to cope with data sovereignty challenges. We introduce a common scenario for the Industry 4.0 age, in which a supplier and a…
View article: Self-supervised Transfer Learning for Instance Segmentation through\n Physical Interaction
Self-supervised Transfer Learning for Instance Segmentation through\n Physical Interaction Open
Instance segmentation of unknown objects from images is regarded as relevant\nfor several robot skills including grasping, tracking and object sorting.\nRecent results in computer vision have shown that large hand-labeled datasets\nenable …
View article: Self-supervised Transfer Learning for Instance Segmentation through Physical Interaction
Self-supervised Transfer Learning for Instance Segmentation through Physical Interaction Open
Instance segmentation of unknown objects from images is regarded as relevant for several robot skills including grasping, tracking and object sorting. Recent results in computer vision have shown that large hand-labeled datasets enable hig…
View article: Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control
Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control Open
We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for reinforcement learning in the presence of sparse rewards. Rather than designing shaped reward functions, ACGD adaptively sets the appropriate task difficulty for the …
View article: Environmental Aware Vulnerability Scoring
Environmental Aware Vulnerability Scoring Open
When assessing the CVSS value of a vulnerability, the Environmental Metrics are often ignored. There are several reasons for this. However, this score is essential for the prioritization of vulnerabilities. The author proposes an approach …
View article: A Systematic Approach toward Extracting Technically Enforceable Policies from Data Usage Control Requirements
A Systematic Approach toward Extracting Technically Enforceable Policies from Data Usage Control Requirements Open
Solutions for data sovereignty are in high demand as companies are willing to exchange their data in decentralized infrastructures. Data sovereignty is tightly coupled with data security and therefore, with data usage control policy specif…
View article: Adaptive Curriculum Generation from Demonstrations for Sim-to-Real\n Visuomotor Control
Adaptive Curriculum Generation from Demonstrations for Sim-to-Real\n Visuomotor Control Open
We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for\nreinforcement learning in the presence of sparse rewards. Rather than designing\nshaped reward functions, ACGD adaptively sets the appropriate task difficulty\nfor t…
View article: Optimization Beyond the Convolution: Generalizing Spatial Relations with End-to-End Metric Learning
Optimization Beyond the Convolution: Generalizing Spatial Relations with End-to-End Metric Learning Open
To operate intelligently in domestic environments, robots require the ability to understand arbitrary spatial relations between objects and to generalize them to objects of varying sizes and shapes. In this work, we present a novel end-to-…
View article: From Plants to Landmarks: Time-invariant Plant Localization that uses Deep Pose Regression in Agricultural Fields
From Plants to Landmarks: Time-invariant Plant Localization that uses Deep Pose Regression in Agricultural Fields Open
Agricultural robots are expected to increase yields in a sustainable way and automate precision tasks, such as weeding and plant monitoring. At the same time, they move in a continuously changing, semi-structured field environment, in whic…
View article: Deep Detection of People and their Mobility Aids for a Hospital Robot
Deep Detection of People and their Mobility Aids for a Hospital Robot Open
Robots operating in populated environments encounter many different types of people, some of whom might have an advanced need for cautious interaction, because of physical impairments or their advanced age. Robots therefore need to recogni…
View article: Optimization Beyond the Convolution: Generalizing Spatial Relations with\n End-to-End Metric Learning
Optimization Beyond the Convolution: Generalizing Spatial Relations with\n End-to-End Metric Learning Open
To operate intelligently in domestic environments, robots require the ability\nto understand arbitrary spatial relations between objects and to generalize\nthem to objects of varying sizes and shapes. In this work, we present a novel\nend-…
View article: The Freiburg Groceries Dataset
The Freiburg Groceries Dataset Open
With the increasing performance of machine learning techniques in the last few years, the computer vision and robotics communities have created a large number of datasets for benchmarking object recognition tasks. These datasets cover a la…
View article: Multimodal deep learning for robust RGB-D object recognition
Multimodal deep learning for robust RGB-D object recognition Open
Robust object recognition is a crucial ingredient of many, if not all, real-world robotics applications. This paper leverages recent progress on Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture for object recogn…