Towards Persistent Storage and Retrieval of Domain Models using Graph Database Technology

We employ graph database technology to persistently store and retrieve robot domain models.Comment: Presented at DSLRob 2015 (arXiv:1601.00877

A Platform-independent Programming Environment for Robot Control

The development of robot control programs is a complex task. Many robots are different in their electrical and mechanical structure which is also reflected in the software. Specific robot software environments support the program development, but are mainly text-based and usually applied by experts in the field with profound knowledge of the target robot. This paper presents a graphical programming environment which aims to ease the development of robot control programs. In contrast to existing graphical robot programming environments, our approach focuses on the composition of parallel action sequences. The developed environment allows to schedule independent robot actions on parallel execution lines and provides mechanism to avoid side-effects of parallel actions. The developed environment is platform-independent and based on the model-driven paradigm. The feasibility of our approach is shown by the application of the sequencer to a simulated service robot and a robot for educational purpose

RoCKIn@Work: Industrial Robot Challenge

RoCKIn@Work was focused on benchmarks in the domain of industrial robots. Both task and functionality benchmarks were derived from real world applications. All of them were part of a bigger user story painting the picture of a scaled down real world factory scenario. Elements used to build the testbed were chosen from common materials in modern manufacturing environments. Networked devices, machines controllable through a central software component, were also part of the testbed and introduced a dynamic component to the task benchmarks. Strict guidelines on data logging were imposed on participating teams to ensure gathered data could be automatically evaluated. This also had the positive effect that teams were made aware of the importance of data logging, not only during a competition but also during research as useful utility in their own laboratory. Tasks and functionality benchmarks are explained in detail, starting with their use case in industry, further detailing their execution and providing information on scoring and ranking mechanisms for the specific benchmark

Evaluating Uncertainty Estimation Methods on 3D Semantic Segmentation of Point Clouds

Deep learning models are extensively used in various safety critical applications. Hence these models along with being accurate need to be highly reliable. One way of achieving this is by quantifying uncertainty. Bayesian methods for UQ have been extensively studied for Deep Learning models applied on images but have been less explored for 3D modalities such as point clouds often used for Robots and Autonomous Systems. In this work, we evaluate three uncertainty quantification methods namely Deep Ensembles, MC-Dropout and MC-DropConnect on the DarkNet21Seg 3D semantic segmentation model and comprehensively analyze the impact of various parameters such as number of models in ensembles or forward passes, and drop probability values, on task performance and uncertainty estimate quality. We find that Deep Ensembles outperforms other methods in both performance and uncertainty metrics. Deep ensembles outperform other methods by a margin of 2.4% in terms of mIOU, 1.3% in terms of accuracy, while providing reliable uncertainty for decision making.Comment: 12 pages, 19 figures, ICML 2020 Workshop on Uncertainty and Robustness in Deep Learnin

Cre-dependent DREADD (Designer Receptors Exclusively Activated by Designer Drugs) mice: Conditional DREADD Mice

DREADDs, designer receptors exclusively activated by designer drugs, are engineered G protein-coupled receptors (GPCR) which can precisely control GPCR signaling pathways (for example, Gq, Gs and Gi). This chemogenetic technology for control of GPCR signaling has been successfully applied in a variety of in vivo studies, including in mice, to remotely control GPCR signaling, for example, in neurons, glia cells, pancreatic beta-cells, or cancer cells. In order to fully explore the in vivo applications of the DREADD technology we generated hM3Dq and hM4Di strains of mice which allow for Cre recombinase-mediated restricted expression of these pathway-selective DREADDs. With the many Cre driver lines now available, these DREADD lines will be applicable to studying a wide array of research and preclinical questions

Comparative genetic analysis: the utility of mouse genetic systems for studying human monogenic disease

One of the long-term goals of mutagenesis programs in the mouse has been to generate mutant lines to facilitate the functional study of every mammalian gene. With a combination of complementary genetic approaches and advances in technology, this aim is slowly becoming a reality. One of the most important features of this strategy is the ability to identify and compare a number of mutations in the same gene, an allelic series. With the advent of gene-driven screening of mutant archives, the search for a specific series of interest is now a practical option. This review focuses on the analysis of multiple mutations from chemical mutagenesis projects in a wide variety of genes and the valuable functional information that has been obtained from these studies. Although gene knockouts and transgenics will continue to be an important resource to ascertain gene function, with a significant proportion of human diseases caused by point mutations, identifying an allelic series is becoming an equally efficient route to generating clinically relevant and functionally important mouse models

Towards Persistent Storage and Retrieval of Domain Models using Graph Database Technology

We employ graph database technology to persistently store and retrieve robot domain models

Light-Emitting Channelrhodopsins for Combined Optogenetic and Chemical-Genetic Control of Neurons

10.1371/journal.pone.0059759PLoS ONE83