The ability for computers to interpret and utilise computerised building regulations is crucial for automated compliance checking of buildings, enabling a faster and more objective building permit review. Since natural language regulations are often not designed to be represented formally, integrating formal representations into the regulation drafting process could drive this change. Through in-depth qualitative expert interviews with regulators worldwide, this study examines the drafting process of building regulations. It aims to establish a methodology for developing formal representations in parallel with natural language versions. To be able to do so, the first step is to understand the current workflow and its requirements. The interviewees in this study highlighted several benefits of parallel drafting, including improved regulation quality and reduced inconsistencies. However, they also acknowledged challenges such as the difficulty of modifying existing laws and the significant time and costs required for such changes. This study explores various strategies for integrating formal representations into the drafting process of building regulations. Notably, in many countries, significant progress has been made towards automated compliance checking. However, much of this progress has taken place independently of legislative bodies, limiting the potential of automation in the building permit process. Nonetheless, early efforts by legislators to adopt formal representations are emerging. This study aims to raise awareness of these developments and contribute to the broader understanding of how formal representations can enhance the regulation drafting process.

Recent developments show that large transformer-based language models have the capability to generate coherent text and source code in response to user prompts. This capability can be used in the construction domain to interpret building regulations and convert them into a formal representation usable for automated compliance checking. While base-size models can already be taught to perform semantic parsing with decent quality, this paper shows how Intermediate Representations (IRs) can be used to improve the semantic parsing quality. With reversible IRs, the training time was reduced to almost a quarter of the initial duration, and through adding a hierarchical parsing step, improvements of up to 6.6% on F1 scores were reached. Furthermore, intermediate representations provide a novel and interpretable method towards a human-in-the-loop approach for translating building regulations into a formal representation.

Situation Awareness is a prominent concept in the human factors community. It is used to analyze and eliminate common sources of human errors in complex tasks and has seen wide-spread use in many fields, such as aviation, health care or ergonomics. Humans who are situation aware are able to reliably generate competent performance, a skill that is also highly desired for other autonomous agents. Yet, the concept has seen only limited use in robotics. We attest this to a lack of clear definitions which would allow assessing an artificial agents capacity for Situation Awareness. Our major contribution is an application-agnostic definition of the terminology and the processes involved in acquiring Situation Awareness. By integrating our definitions into the perception-action-cycle we provide a connection to the agent's observable behavior. Our second major contribution is a way to estimate, whether an agent has lost Situation Awareness based on surprise. This measure can be used online and does not require explicit or implicit knowledge of the task. We evaluate our concept on a physical workspace built for abstract Human- Robot-Cooperation scenarios.

In Industry 4.0 nodes are becoming interconnected and part of the growing Industrial Internet of Things. The use of a reduced physical layer and emerging real-time Ethernet standards, enables such nodes to be implemented cost-efficiently. Technologies and standards, which focus on cost-efficient Ethernet technologies for the use in vehicles, are quite promising for industrial applications. The integration of such new technologies and nodes in existing installations will lead to mixed systems consisting of conventional Industrial Ethernet equipment and new technologies. For such a migration scenario we present first results and a test system to validate physical layers and transceivers with respect to timing and synchronizing. Live-measurement methods are given for a dedicated real-time Ethernet system that provides detailed information about timing. Within this scope we show that mixed physical layers can be introduced in real-time Ethernet networks without deteriorating timing in real time application.

Multipath interference is inherent to the working principle of a Time-of-flight camera and can influence the measurements by several centimeters. Especially in applications that demand for high accuracy, such as object localization for robotic manipulation or ego-motion estimation of mobile robots, multipath interference is not tolerable. In this paper we formulate a multipath model in order to estimate the interference and correct the measurements. The proposed approach comprises the measured scene structure. All distracting surfaces are assumed to be Lambertian radiators and the directional interference is simulated for correction purposes. The positive impact of these corrections is experimentally demonstrated.

Because bin-picking effectively mirrors great challenges in robotics, it has been a relevant robotic showpiece application for several decades. In this paper we describe the computer vision algorithms in combination with the sophisticated control schemes of the robot and demonstrate a reliable and robust solution to the chosen problem. This paper approaches the bin-picking issue by applying the latest state-of-the-art hardware components, namely an impedance controlled lightweight robot and a Time-of-Flight camera. Lightweight robots have gained new capabilities in both sensing and actuation without suffering a decrease in speed and payload. Time-of- Flight cameras are superior to common proximity sensors in the sense that they provide depth and intensity images in video frame rate independent of textures. The bin-picking solution presented in this paper aims at extending the classical bin-picking problem by incorporating an environment model and allowing for the physical human-robot interaction during the entire process. Existing imprecisions in Time-of-Flight camera measurements and environment uncertainties are compensated by the compliant behavior of the robot. The overall process is implemented in a generic state machine that also monitors the entire bin-picking process.

Research on humanoid robots for use in servicing tasks, e.g. fetching and delivery, attracts steadily more interest. With “Rollin' Justin” a mobile robotic system and research platform is presented that allows sophisticated control algorithms and dexterous manipulation. This video gives an overview of the mobile humanoid robotic system “Rollin' Justin” with special emphasis on mechanical design features, control issues and high-level system capabilities such as human robot interaction.

Time-of-flight cameras constitute a smart and fast technology for 3D perception but lack in measurement precision and robustness. The authors present a comprehensive approach for 3D environment mapping based on this technology. Imprecision of depth measurements are properly handled by calibration and application of several filters. Robust registration is performed by a novel extension to the Iterative Closest Point algorithm. Remaining registration errors are reduced by global relaxation after loop-closure and surface smoothing. A laboratory ground truth evaluation is provided as well as 3D mapping experiments in a larger indoor environment.

Recently, ToF-cameras have attracted attention because of their ability to generate a full 2 1/2D depth image at video frame rates. Thus, ToF-cameras are suitable for real-time 3D tasks such as tracking, visual servoing or object pose estimation. The usability of such systems mainly depends on an accurate camera calibration. In this work a calibration process for ToF-cameras with respect to the intrinsic parameters, the depth measurement distortion and the pose of the camera relative to a robotpsilas end effector is described. The calibration process is not only based on the monochromatic images of the camera but also uses its depth values that are generated from a chequer-board pattern. The robustness and precision of the presented method is assessed applying it to randomly selected shots and comparing the calibrated measurements to a ground truth obtained from a laser scanner.

Classical position-based visual servoing approaches rely on the presence of distinctive features in the image such as corners and edges. In this contribution we exploit a hierarchical approach for object detection, initial-pose estimation, and real-time tracking based first on colour distribution and subsequently on the shape and texture information. The shape model of the object is not limited to surface primitives but allow for any free-form surface not subject to self-occlusion. We evaluate the approach as part of a handshake scenario where a 7-DoF robot takes a free moving object over from a human