In this paper we discuss how the combination of modern technologies in “big data” storage and management, knowledge representation and processing, cloud-based computation, and web technology can help the robotics community to establish and strengthen an open research discipline. We describe how we made the demonstrator of a EU project review openly available to the research community. Specifically, we recorded episodic memories with rich semantic annotations during a pizza preparation experiment in autonomous robot manipulation. Afterwards, we released them as an open knowledge base using the cloud- and web-based robot knowledge service OPENEASE. We discuss several ways on how this open data can be used to validate our experimental reports and to tackle novel challenging research problems.

We present RoboSherlock, an open source software framework for implementing perception systems for robots performing human-scale everyday manipulation tasks. In RoboSherlock, perception and interpretation of realistic scenes is formulated as an unstructured information management (UIM) problem. The application of the UIM principle supports the implementation of perception systems that can answer task-relevant queries about objects in a scene, boost object recognition performance by combining the strengths of multiple perception algorithms, support knowledge-enabled reasoning about objects and enable automatic and knowledge-driven generation of processing pipelines. We demonstrate the potential of the proposed framework by three feasibility studies of systems for real-world scene perception that have been built on top of RoboSherlock.

Many future application scenarios of robotics envision robotic agents to be in close physical interaction with humans: On the factory floor, robotic agents shall support their human co-workers with the dull and health threatening parts of their jobs. In their homes, robotic agents shall enable people to stay independent, even if they have disabilities that require physical help in their daily life - a pressing need for our aging societies. A key requirement for such robotic agents is that they are safety-aware, that is, that they know when actions may hurt or threaten humans and actively refrain from performing them. Safe robot control systems are a current research focus in control theory. The control system designs, however, are a bit paranoid: programmers build “software fences” around people, effectively preventing physical interactions. To physically interact in a competent manner robotic agents have to reason about the task context, the human, and her intentions. In this paper, we propose to extend cognition-enabled robot control by introducing humans, physical interaction events, and safe movements as first class objects into the plan language. We show the power of the safety-aware control approach in a real-world scenario with a leading-edge autonomous manipulation platform. Finally, we share our experimental recordings through an online knowledge processing system, and invite the reader to explore the data with queries based on the concepts discussed in this paper.

Nearest neighbour searches in the image plane are among the most frequent problems in a variety of computer vision and image processing tasks. They can be used to replace missing values in image filtering, or to group close objects in image segmentation, or to access neighbouring points of interest in feature extraction. In particular, we address two nearest neighbour problems: The nearest neighbour problem is usually stated independently of the application as returning the point p ∈ S,S = {(x1,y1), . . . ,(xn,yn)} that minimises the Euclidean distance ||p− q||2 to a query point q = (x,y). The simple solution of a linear scan comprises a comparison of q to all elements of S, which is too time-consuming for most applications, especially those with real-time requirements. If the nearest neighbour p ∈ S must be found for every coordinate q ∈ I of an image I = {(0,0),(0,1),(0,2), . . . ,(W,H)} of width W and height H, we obtain the all nearest neighbours problem. This problem occurs frequently in modern saliency based approaches, where only robustly detectable image regions are processed (for example in SIFT). In this paper, we introduce an approximate solution to solve these problems that is based on using a space filling curve. The central idea of the proposed approach is to map the image plane to one dimension using the Hilbert curve (Fig. 1). The nearest-neighbour problem is then solved efficiently in one dimension and mapped back to the 2D-plane.