We present the design and development of a robotic character performed live on stage in a theatrical play, with a focus on the novel control strategies used by a human puppeteer. The system enables real-time expressive behaviour through five core mapping strategies, including degree-of-freedom (DoF) consolidation, time-based intensity modulation, affective button mapping, motion scrubbing, and a global servo gain control. Beyond artistic applications, these techniques suggest new directions for intuitive and expressive teleoperation in robotics.

This paper discusses how improvisation theatre can be used as a tool for design and (design) education in the field of Human-Robot Interaction. This tool has been explored in settings focused on design education for students as well as care professionals. In this paper, a format and set of guiding principles that were found to work constructively will be presented, as well as a discussion on outcomes, results, and insights from a number of sessions.

This paper discusses how improvisation theatre can be used as a tool for design and (design) education in the field of Human-Robot Interaction. This tool has been explored in settings focused on design education for students as well as care professionals. In this paper, a format and set of guiding principles that were found to work constructively will be presented, as well as a discussion on outcomes, results, and insights from a number of sessions.

Electroactive textile (EAT) has the potential to apply pressure stimuli to the skin, e.g. in the form of a squeeze on the arm. To present a perceivable haptic sensation we need to know the perception threshold for such stimuli. We designed a set-up based on motorized ribbons around the arm with five different widths (range 3 - 49 mm) for psychophysical studies. We investigated the perception threshold of force pressure and ribbon reduction in two studies, using two methods (PSI and 1up/3down staircase), comparing sex, the left and right arm, the lower and upper arm, and stimulated surface area with a total of 57 participants. We found that larger stimulation surfaces require less pressure to reach the perception threshold (0.151 N per cm $^{2}$ for 3 mm width, 0.00972 N per cm $^{2}$ for 49 mm width on the lower arm). This indicates a spatial summation effect for these pressure stimuli. We did not find significant differences in perception threshold for the left and right arm and, the upper and lower arm. Between male and female participants we found significant differences for two conditions (10 mm and 25 mm) in Experiment 1, but we could not reproduce this in Experiment 2.

To identify voids around underground concrete sewer pipes, we investigate an automated in-pipe detection method. This method uses a robotic platform with a mechanical impactor to generate non-destructive stress waves at any angular position on the pipe's inner circumference. The resulting acoustic waves are recorded with a microphone and are analyzed to make a void location map around the pipe. To characterize the method, we developed numerical models where we examine the influence of void width, height, and shape, and the thickness of the pipe and the presence of reinforcement in the pipe on the void detection. We validated the proposed method by conducting experiments inside a concrete pipe with custom-made voids, and differently compacted areas in the pipe’s surrounding. The results showed that the method was able to detect voids and areas with less compacted sand around the pipe.

Social touch technology, haptic technology to mediate social touch interactions, potentially contributes to reducing negative effects of skin hunger and social isolation. This field is developing and while there are a number of prototypes, few became products and less persisted in the market today. Viable social touch technology is essential for research on social touch and it has an unexplored market potential. Making prototypes and evaluating them is the approach of generating knowledge in Research through Design (RtD). In RtD, researchers investigate the speculative future, probing on what the world could and should be, leaving the exact method of designing prototypes open. One possible method is tinkering, characterized by a playful and creative exploration. Tinkering environments, however, need a careful design of toolkits and setting. In this study, we report on the toolkit and setup we used for a tinkering-based teaching unit on social touch technology, held within an introductory course of an Interaction Technology master program, and describe the resulting prototypes. With a qualitative analysis of the results, we consider the teaching unit as a success, w.r.t. the diversity of the concepts developed. Tinkering is well-known as a playful method for education in Science, Technology, Engineering, and Maths, aiming at school children and high school students. It is not yet established as a design method in itself, and not considered as element of an academic skill set. Here, we argue that tinkering is a valuable design method in the context of social touch technology, and that it has a place in the design approaches within an academic setting. In a further step, we also want to include experts from other domains in the design process, such as psychologists or fashion designers. For that end, we suggest expanding a current toolkit for wearable technology with concepts from the teaching unit, more scaffolding tools, a variety of tactile actuators, and a software tool that allow for (re)configuring designs rather than programing them.

There is growing interest in psychological interventions using socially assistive robots to mitigate distress and pain in the pediatric population. This work seeks to address the deficit in understanding of what features and functionality young children and their parents desire to help with pain management by using co-design, a common approach to exploring participants' imaginations and gathering design requirements. To close this gap, we carried out a co-design workshop involving seven families (with children aged between 4-6 and their parents) to understand their expectations and design preferences for a robot designed for pain management in children. Data were collected from surveys, video and audio recordings, interviews, and field notes. We present the robot prototypes constructed during the workshops and derive several preferences of the children (e.g., zoomorphic shape, distractors and emotional expressions as behaviors). Additionally, we report methodological insights regarding the involvement of young children and their parents in the co-design process. Based on the findings of this co-design study, we discuss personalization as a possible design concept for future child-robot interaction development.

Inspection and maintenance are two crucial aspects of industrial pipeline plants. While robotics has made tremendous progress in the mechanic design of in-pipe inspection robots, the autonomous control of such robots is still a big open challenge due to the high number of actuators and the complex manoeuvres required. To address this problem, we investigate the usage of Deep Reinforcement Learning for achieving autonomous navigation of in-pipe robots in pipeline networks with complex topologies. We introduce a hierarchical policy decomposition based on Hierarchical Reinforcement Learning to learn robust high-level navigation skills. We show that the hierarchical structure introduced in the policy is fundamental for solving the navigation task through pipes and necessary for achieving navigation performances superior to human-level control. A video of our experiments can be found at: https://youtu.be/uyjSHulpGoI .

Detecting voids in pipe surroundings is essential to structural condition assessment of concrete sewer pipelines. Impact-echo is a non-destructive testing method that can be used for this purpose. This method works based on exciting the surface of concrete and using a contact-based sensor to monitor the propagation of the resulting stress waves. However, the presence of deposits and humidity inside the sewer pipe makes establishing a contact between the sensor and the pipe wall very difficult. Therefore, the goal of this study is to compare the performance of contactless sensors for this application. Specifically, we assess how microphones, laser vibrometers, and particle velocity meters support void detection. To this end, we first investigate the requirements for excitation of stress waves in the concrete in terms of impact duration and energy. Next, we suggest a data analysis method for void detection based on the difference in the acoustic impedances of concrete, sand, and air. Both numerical modeling and experimental results show the supremacy of microphones in detecting voids behind concrete. We suggest that future studies conduct in-situ experiments to explore how pipe wall reflections and noise influence the performance of a microphone in detecting voids surrounding the concrete sewer pipes.

Data-driven sewer asset management uses digital sewer representations to store inspection data and to support predictive maintenance planning. This approach requires asset managers to determine what inspection data they need to collect for the assessment of the asset conditions. Existing studies review sewer inspection methods based on their technical working principles but do not explicitly address what data about condition cues these methods provide. Consequently, literature lacks structured insights that help sewer asset managers link their data-needs with appropriate condition assessment methods. To make this link, we propose a data-needs based categorization of sewer inspection methods. Specifically, we relate data output of inspection methods to condition cues using the classification of hydraulic, structural, and environmental inspection domains. This shows that few methods exist to collect data about cues in structural and environmental domains. Future research should develop methods to satisfy these needs, and eventually, contribute to holistic data-driven asset management.

Our university offers an IT-based design programme, with an engineering background in Computer Science and Electrical Engineering.Its focus on design and creativity, as well as the diversity of the students, requires an approach in informatics courses different from classical computer science programmes.While tinkering is an increasingly popular approach in STEM stimulation and education outside university, we argue that also in an academic setting a tinkering mindset has a relevant contribution.In this paper, we identify key elements in setting up tinkering sessions and report on their implementation for a course on algorithms.We will present and discuss results and observations of our teaching method, that are promising to continue and extend the tinkering approach in an academic setting.

Summary Ground Penetrating Radar (GPR) is an electromagnetic inspection method that is widely used to help locate and assess conditions of sewer pipes. However, when operated from the ground surface, the method is not efficient in identifying early stages of void formation, which if untreated, can lead to the appearance of sinkholes. To remedy this issue, a few studies have introduced in-pipe GPR inspection systems to identify structural defects and the voids behind the sewer wall. Still, less research has focused on studying the implications of emission, propagation, and reception of electromagnetic waves in the enclosed environment of sewer pipes and their subsequent impact on the resulting radargrams. In this study, we address this matter by modeling and comparing equivalent planar (ground-surface) and cylindrical (in-pipe) operation environments. Our results indicate the differences between the radargrams of the cylindrical and planar topologies in terms of intensity, the slope of hyperbolas, and presence of horizontal reflection lines. These results encourage practical considerations for conducting in-pipe GPR surveys with respect to choosing antenna separation and target depth of the survey and tuning the parameters for hyperbolas in automatic object detection algorithms.

Autism impacts around 5 million people in the EU (Autism-Europe). Research has shown that social robots, due to their deterministic nature, simplified appearance and technological capabilities, can enable therapy or become assistive technology for empowering autistic individuals with household activities. Consequently, toolkits have emerged for prototyping social robots. Regarding such toolkits, there is a methodological, inclusion gap: there is no comprehensive, scaffolded co-design process to include cognitively disadvantaged users in decision-making regarding robots’ fundamental design choices. To overcome this gap and empower autistic adults to truly design their own (non-preprogrammed) robots, this research explores a social robot toolkit driven by designerly scaffoldings for driving participatory design activities.

According to Autism-Europe, autism impacts around 5 million people in the EU. Recent research has shown that social robots, due to their deterministic nature, simplified appearance and technological capabilities, can enable robot- assisted therapy or act as assistive technology for empowering autistic individuals with daily household activities. As such, toolkits have emerged to enable researchers to prototype assistive social robots. In the design and research regarding such toolkits, there are gaps regarding robot designs, fundamental customization possibilities and especially the methodologies for operationalizing and scaffolding the co-design of social robots with vulnerable groups. In order to take a first step towards overcoming these research/design gaps and towards uncovering the right questions about them, the Co3 Project deals with an exploratory study involving the participatory design of a social robot toolkit for and with autistic adults. The project’s components have been co-designed, evaluated and tested with autistic adults at an autism care institute. The exploratory project has carved a toolkit of linkable social robot building blocks centered around which is a holistic, novel process for conducting social robot participatory design with cognitively impaired individuals. That process has artefacts meticulously designed with the participants in mind–giving the artefacts sufficient scaffolding to make co- design navigable by bridging the imaginative or social impairments of involved participants. The project aims to inspire a movement of scalable, democratized social robot co-design, which can evoke questions on what human-robot interactions to design in the first place and which can empower egalitarian inclusiveness in (co-)design of all users.

Autism impacts 5 million people in the EU. Research has shown social robots as enabling robot-assisted therapy or providing assistance in everyday activities. However, given the strong heterogeneity of the target group, it proves to be difficult to design generic, one-size-fits-all assistive applications. Various toolkits have emerged for self-building of robots, yet these still require considerable technical skill. More importantly, such toolkits lack guidance in a structured design process, to uncover and translate real needs into coherent product concepts that can actually be built. To fill this gap, we engaged in CoCoCo (Co3), an exploratory study to empower autistic adults to truly design their own (non-preprogrammed) collaborative robots. The Co3 toolkit of linkable building blocks guides designer and autistic participant through an iterative co-design process. The toolkit itself has been co-designed, evaluated and tested with autistic adults at a FabLab-inspired activity centre for autistic individuals. We discuss how the toolkit elements are padded with cognitive and communicative scaffolding to bridge imagination and communication-related gaps in the interaction between designer and autistic participant. We present Co3, a first step in open-source, scalable, democratized design of social assistive robots, with the aim to increase inclusiveness and democratization.

Awe is a heightened emotional state of fear and wonder that creates a physiological response resulting in a cascade of hairs standing on end, also known as piloerection or goose-bumps. This latent sense once served an animalian purpose of survival, but now lies dormant and is often not experienced consciously. In fact, 55 percent of the population reports to not feel this sensation that is noted to be healthy. The AWE Goosebumps artifact is an emotion prosthesis that animates the latent sensation of awe for embodiment and externalizes cues for communication. As the sensation is not experienced consciously, the techno fashion invites an opportunity to be a second skin for frisson biofeedback, behavior training, and expression to others as a tool to transform the doldrums of modern day to performative states of wonder.

In this chapter we discuss principles, properties, and applications of piezoelectric sensors. A description of the piezoelectric effect is followed by an overview of the most common piezoelectric parameters. Piezoelectric materials are used in force sensors and accelerometers, the subject of the second section. Interface circuits for piezoelectric sensors are treated next, and various specific and original applications, including tactile sensors, are listed in the last section of this chapter.

Inductive and magnetic sensors employ variables and parameters like magnetic induction, magnetic flux, self-inductance, mutual inductance or magnetic resistance. By a particular construction of the device, these quantities are made dependent on an applied displacement or force. First we review various magnetic quantities and their relations. Next the operation and specifications of the major types of magnetic and inductive sensors are analysed, including Hall sensors, fluxgate sensors, eddy current sensors and magnetostrictive sensors. Special attention is given to transformer-type sensors (e.g. LVDT and resolvers). The chapter concludes with a section on applications, in which inductive and magnetic sensors are used to solve particular measurement problems.