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

Maximum Likelihood Uncertainty Estimation: Robustness to Outliers

We benchmark the robustness of maximum likelihood based uncertainty estimation methods to outliers in training data for regression tasks. Outliers or noisy labels in training data results in degraded performances as well as incorrect estimation of uncertainty. We propose the use of a heavy-tailed distribution (Laplace distribution) to improve the robustness to outliers. This property is evaluated using standard regression benchmarks and on a high-dimensional regression task of monocular depth estimation, both containing outliers. In particular, heavy-tailed distribution based maximum likelihood provides better uncertainty estimates, better separation in uncertainty for out-of-distribution data, as well as better detection of adversarial attacks in the presence of outliers

How Trump Happened: Rezension zu "Identity Crisis: The 2016 Presidential Elections and the Battle for the Meaning of America" von John Sides, Michael Tesler und Lynn Vavreck

John Sides, Michael Tesler, Lynn Vavreck: Identity Crisis - The 2016 Presidential Elections and the Battle for the Meaning of America. Princeton, NJ: Princeton University Press 2018. 978-0-691-19643-

Neural Semantic Parsing for Syntax-Aware Code Generation

The task of mapping natural language expressions to logical forms is referred to as semantic parsing. The syntax of logical forms that are based on programming or query languages, such as Python or SQL, is defined by a formal grammar. In this thesis, we present an efficient neural semantic parser that exploits the underlying grammar of logical forms to enforce well-formed expressions. We use an encoder-decoder model for sequence prediction. Syntactically valid programs are guaranteed by means of a bottom-up shift-reduce parser, that keeps track of the set of viable tokens at each decoding step. We show that the proposed model outperforms the standard encoder-decoder model across datasets and is competitive with comparable grammar-guided semantic parsing approaches

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

Mammalian genes induce partially reprogrammed pluripotent stem cells in non-mammalian vertebrate and invertebrate species.

Cells are fundamental units of life, but little is known about evolution of cell states. Induced pluripotent stem cells (iPSCs) are once differentiated cells that have been re-programmed to an embryonic stem cell-like state, providing a powerful platform for biology and medicine. However, they have been limited to a few mammalian species. Here we found that a set of four mammalian transcription factor genes used to generate iPSCs in mouse and humans can induce a partially reprogrammed pluripotent stem cell (PRPSCs) state in vertebrate and invertebrate model organisms, in mammals, birds, fish, and fly, which span 550 million years from a common ancestor. These findings are one of the first to show cross-lineage stem cell-like induction, and to generate pluripotent-like cells for several of these species with in vivo chimeras. We suggest that the stem-cell state may be highly conserved across a wide phylogenetic range. DOI:http://dx.doi.org/10.7554/eLife.00036.001

A Survey on Domain-Specific Languages in Robotics

Nordmann A, Hochgeschwender N, Wrede S. A Survey on Domain-Specific Languages in Robotics. In: International Conference on Simulation, Modeling, and Programming for Autonomous Robots. 2014.The design, simulation and programming of robotics systems is challenging as expertise from multiple domains needs to be integrated conceptually and technically. Domain-specific modeling promises an efficient and flexible concept for developing robotics applications that copes with this challenge. It allows to raise the level of abstraction through the use of specific concepts that are closer to the respective domain concerns and easier to understand and validate. Furthermore, it focuses on increasing the level of automation, e.g. through code generation, to bridge the gap between the modeling and the implementation levels and to improve the efficiency and quality of the software development process. Within this contribution, we survey the literature available on domain-specific (modeling) languages in robotics required to realize a state-of-the-art real-world example from the RoboCup@Work competition. We classify 41 publications in the field as reference for potential DSL users. Furthermore, we analyze these contributions from a DSL-engineering viewpoint and discuss quantitative and qualitative aspects such as the methods and tools used for DSL implementation as well as their documentation status and platform integration. Finally, we conclude with some recommendations for discussion in the robotics programming and simulation community based on the insights gained with this survey