Generating Annotated Training Data for 6D Object Pose Estimation in Operational Environments with Minimal User Interaction

Recently developed deep neural networks achieved state-of-the-art results in the subject of 6D object pose estimation for robot manipulation. However, those supervised deep learning methods require expensive annotated training data. Current methods for reducing those costs frequently use synthetic data from simulations, but rely on expert knowledge and suffer from the "domain gap" when shifting to the real world. Here, we present a proof of concept for a novel approach of autonomously generating annotated training data for 6D object pose estimation. This approach is designed for learning new objects in operational environments while requiring little interaction and no expertise on the part of the user. We evaluate our autonomous data generation approach in two grasping experiments, where we archive a similar grasping success rate as related work on a non autonomously generated data set.Comment: This is a preprint and currently under peer review at IROS 202

MVIP: A Dataset for Industrial Part Recognition

We present MVIP, a novel dataset for multi-modal and multi-view application oriented industrial part recognition. Here we combine a calibrated RGBD multi-view dataset with additional object context such as physical properties, natural language, and super-classes. Our main goal with MVIP is to study and push transferability of various state-of-the-art methods within related downstream tasks towards an efficient deployment of industrial classifiers. Additionally, we intent to push with MVIP research regarding several modality fusion topics, (automated) synthetic data generation, and complex data sampling methods -- combined in a single application oriented benchmark

InVar-100: Industrial Objects in Varied Contexts Dataset

The Industrial Objects in Varied Contexts (InVar) dataset was internally produced by our team and contains 100 objects in 20800 total images (208 images per class). The objects consist of common automotive, machine and robotics lab parts. Each class contains 4 sub-categories (52 images each) with different attributes and visual complexities. White background (Dwh): The object is against a clean white background and the object is clear, centred and in focus. Stationary Setup (Dst): These images are also taken against a clean background using a stationary camera setup, with uncentered objects at a constant distance. The images have lower DPI resolution with occasional cropping. Handheld (Dha): These images are taken with the user holding the objects, with occasional occluding. Cluttered background (Dcl): These images are taken with the object placed along with other objects from the lab in the background and with no occlusion. The dataset was produced to simulate the miscellaneous issues in industrial setups as discussed. The dataset was produced by our staff at different workstations and labs in Berlin. More details regarding the objects used for digitisation are available in the metadata file

Towards Our Common Digital Future. Flagship Report.

In the report “Towards Our Common Digital Future”, the WBGU makes it clear that sustainability strategies and concepts need to be fundamentally further developed in the age of digitalization. Only if digital change and the Transformation towards Sustainability are synchronized can we succeed in advancing climate and Earth-system protection and in making social progress in human development. Without formative political action, digital change will further accelerate resource and energy consumption, and exacerbate damage to the environment and the climate. It is therefore an urgent political task to create the conditions needed to place digitalization at the service of sustainable development

Unsere gemeinsame digitale Zukunft

Das Gutachten „Unsere gemeinsame digitale Zukunft“ macht deutlich, dass Nachhaltigkeitsstrategien und -konzepte im Zeitalter der Digitalisierung grundlegend weiterentwickelt werden müssen. Nur wenn der digitale Wandel und die Transformation zur Nachhaltigkeit konstruktiv verzahnt werden, kann es gelingen, Klima- und Erdsystemschutz sowie soziale Fortschritte menschlicher Entwicklung voranzubringen. Ohne aktive politische Gestaltung wird der digitale Wandel den Ressourcen- und Energieverbrauch sowie die Schädigung von Umwelt und Klima weiter beschleunigen. Daher ist es eine vordringliche politische Aufgabe, Bedingungen dafür zu schaffen, die Digitalisierung in den Dienst nachhaltiger Entwicklung zu stellen

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment

Deep learning for part identification based on inherent features

The identification of parts is essential for the efficient automation of logistic processes such as part supply in assembly and disassembly. This paper describes a new method for the optical identification of parts without explicit codes but based on inherent geometrical features with Deep Learning. The paper focusses on the improvement of training of Deep Learning systems taking into account conflicting factors such as limited training data and high variety of parts. Based on a case study in turbine industry the effects of steadily growing training data on the robustness of part classification are evaluated

The 2011-2012 pilot European Society of Cardiology Sentinel Registry of Transcatheter Aortic Valve Implantation: 12-month clinical outcomes.

AIMS Our aim was to assess one-year outcomes of patients enrolled in the pilot European Sentinel Registry of Transcatheter Aortic Valve Implantation (TAVI). METHODS AND RESULTS One-year outcomes of 4,571 patients (81.4±7.2 years, 2,291 [50.1%] male) receiving TAVI with the SAPIEN XT (57.3%) or CoreValve prosthesis at 137 European centres were analysed using Kaplan-Meier and Cox proportional hazards regression techniques. At one year, 3,341 patients were alive, 821 had died, and 409 were lost to follow-up. Of 2,125 patients who underwent functional assessment, 1,916 (90%) were in New York Heart Association (NYHA) Class I/II at one year, with functional improvement from baseline noted in 1,682 patients (88%). One-year survival based on 4,564 patients was estimated at 79.1%. Independent baseline predictors of mortality were increasing age and logistic EuroSCORE, the presence of NYHA III/IV, chronic obstructive pulmonary disease, and atrial fibrillation. Female gender was associated with a 4% survival benefit at one year. Vascular access routes other than transfemoral were associated with poorer survival. Procedural failure and major periprocedural complications had an adverse impact on survival. CONCLUSIONS Contemporary European experience attests to the effectiveness of routine TAVI in unselected elderly patients