Task-oriented object grasping and rearrangement are key skills for robots, which have to perform versatile real-world manipulation tasks. However, they remain challenging due to partial observations of the objects and shape variations in categorical objects. In this paper, we present the Multi-feature Implicit Model (MIMO), a novel object representation that encodes multiple spatial features between a point and an object in an implicit neural field. Training such a model on multiple features ensures that it embeds the object shapes consistently in different aspects, thus improving its performance in object shape reconstruction from partial observation, shape similarity measure, and modeling spatial relations between objects. Based on MIMO, we propose a framework to learn task-oriented object grasping and rearrangement from single or multiple human demonstration videos. The evaluations in simulation show that our approach outperforms the state-of-the-art methods for multi- and single-view observations. Real-world experiments demonstrate the efficacy of our approach in one- and few-shot imitation learning of manipulation tasks.

To perform versatile mobile manipulation tasks in human-centered environments, the ability to efficiently transfer learned skills, knowledge, and experiences from one robot to another or across different environments is critical. In this paper, we present MAkEable, a versatile uni- and multi-manual mobile manipulation framework that facilitates the transfer of capabilities and knowledge across different tasks, environments, and robots. Our framework integrates an affordance-based task description into the memory-centric cognitive architecture of the ARMAR humanoid robot family, which supports the sharing of experiences and demonstrations for transferring mobile manipulation skills. By representing mobile manipulation actions through affordances, i. e., interaction possibilities of the robot with its environment, we provide a unifying framework for the autonomous uni- and multi-manual manipulation of known and unknown objects in various environments. We demonstrate MAkEable’s applicability in real-world experiments for multiple robots, tasks, and environments. This includes grasping known and unknown objects, object placing, bimanual object grasping, memory-enabled skill transfer in a drawer opening scenario across two different humanoid robots, and a pouring task learned from human demonstration. Code is available through our project page1.

For a robot to perform a grasping and manipulation task, it has to determine possible robot placements in the workspace, from which target objects or environmental elements relevant to the given task are reachable. This work presents a novel approach for finding placements for the mobile base of a humanoid robot in an unknown environment with multiple support planes. We propose a novel type of reachability map - the Oriented Surface Reachability Map - that takes inclined surfaces in the environment into account and has the same complexity as reachability maps designed for flat surfaces. The resulting robot placements are not limited to SE(2) but can be applied to arbitrarily oriented planes in 3D space. The proposed method was evaluated in simulation and on the humanoid robot ARMAR-6 in real-world grasping experiments. The results show that a placement can be found for over 80% of the poses that are reachable in complicated, simulated environments, with only a small runtime overhead.

In real world applications, robotic solutions remain impractical due to the challenges that arise in unknown and unstructured environments. To perform complex manipulation tasks in complex and cluttered situations, robots need to be able to identify the interaction possibilities with the scene, i.e. the affordances of the objects encountered. In unstructured environments with noisy perception, insufficient scene understanding and limited prior knowledge, this is a challenging task. In this work, we present an approach for grasping unknown objects in cluttered scenes with a humanoid robot in the context of a nuclear decommissioning task. Our approach combines the convenience and reliability of autonomous robot control with the precision and adaptability of teleoperation in a semi-autonomous selection of grasp affordances. Additionally, this allows exploiting the expert knowledge of an experienced human worker. To evaluate our approach, we conducted 75 real world experiments with more than 660 grasp executions on the humanoid robot ARMAR-6. The results demonstrate that high-level decisions made by the human operator, supported by autonomous robot control, contribute significantly to successful task execution.

The cloud computing paradigm is one of the most promising of its kind. The demand and supply of online delivered software have been continuously growing. However, reports have shown that various Software as a service solutions have not succeeded in reaching cloud user expectations. Hence, users need to be satisfied with the service quality which is determined by the fulfillment of both functional and non-functional requirements. Currently, the evaluation of the cloud service quality consider-ing individual requirements is up to the user. Unfortunately, the amount of non-functional quality criteria is very high and some are laborious to determine. We believe that an information system could support and enable users without specific knowledge to evaluate comprehensively and rapidly the quality of their cloud. In this paper we first present a quality model for SaaS. In addition, we demonstrate how the quality information should be structured and visualized in a user-friendly way. With the provided transparency users are able to establish quality awareness in the long term.

Domain analysis involves not only looking at standard requirements documents (e.g., use case specifications) but also at customer information packs, market analyses, etc. Looking across all these documents and deriving, in a practical and scalable way, a feature model that is comprised of coherent abstractions is a fundamental and non-trivial challenge. We conduct an exploratory study to investigate the suitability of Information Retrieval (IR) techniques for scalable identification of commonalities and variabilities in requirement specifications for software product lines. Accordingly, based on observations derived from industrial experience and on state-of-the-art research and practice, we also propose an initial framework, leveraging IR to systematically abstract requirements from existing specifications of a given domain into a feature model. We evaluate this framework, present a roadmap for its further extension, and formulate hypotheses to guide future work in exploring IR techniques for domain analysis.

Component-based applications require runtime support to be able to guarantee non-functional properties. This paper proposes an architecture for a real-time-capable, component-based runtime environment, which allows to separate non-functional and functional concerns in component-based software development. The architecture is presented with particular focus on the real-time-non-real-time split of the runtime environment and the communication issues of respective component types and container parts.

For quite some time now, applications have been designed and developed in various projects of our research group by using middleware. In addition, various middleware products have been evaluated. An existing client/server system has been converted to EJB. The subject of this paper will be the results of these studies, along with the characteristics of the analyzed servers other concepts and related work.