Ewald Strasser

Dimensions of anthropomorphism: from humanness to humanlikeness

In HRI anthropomorphism has been considered to be a unidimensional construct. However, social psychological studies of the potentially reverse process to anthropomorphisation - known as dehumanization - indicate that there are two distinct senses of humanness with different consequences for people who are dehumanized by deprivation of some of the aspects of these dimensions. These attributes are crucial for perception of others as humans. Therefore, we hypothesized that the same attributes could be used to anthropomorphize a robot in HRI and only a two-dimensional measures would be suitable to distinguish between different forms of making a robot more humanlike. In a study where participants played a quiz based on the TV show “Jeopardy!” we manipulated a NAO robot’s intelligence and emotionality. The results suggest that only emotionality, not intelligence, makes robots be perceived as more humanlike. Furthermore, we found some evidence that anthropomorphism is a multidimensional phenomenon.Categories and Subject DescriptorsI.2.9 [Computing Methodologies]: ARTIFICIAL INTELLIGENCE—Robotics

Studies in public places as a means to positively influence people's attitude towards robots

It is the aim of this paper to show on a meta-level how studies in public places can contribute to positively influence peoples attitude towards robots. By means of examining objective and subjective data gathered in the lab and data from field studies, it will be shown how peoples experiences with a robot outside the sheltering laboratory surroundings can help to value robots more positively. We argue, that studies in public places can serve as a means to enable many people with hands-on experiences and as proof-of-concept evaluation for researchers. We contrasted peoples explicit ratings of our robots and although the differences are rather subtle, they nevertheless reveal a tendency for the positive effect of field studies in public places. Additionally, we contrasted peoples implicit attitude towards robots which could support our assumption that people who interacted with robots in the field rate it significantly better than people who interacted with it in the lab.

The Interactive Urban Robot: User-centered development and final field trial of a direction requesting robot

Abstract In this article, we present the user-centered development of the service robot IURO. IURO’s goal is to find the way to a designated place in town without any previous map knowledge, just by retrieving information from asking pedestrians for directions. We present the 3-years development process,which involved a series of studies on its appearance, communication model, feedback modalities, and social navigation mechanisms. Our main contribution lies within the final field trial.With the autonomous IURO platform, we performed a series of six way-finding runs (over 24 hours of run-time in total) in the city center of Munich, Germany. The robot interacted with approximately 100 pedestrians of which 36 interactions included a full route dialogue. A variety of empirical methods was used to explore reactions of primary users (pedestrians who actually interacted with the robot) and secondary users (bystanders who observed others interacting). The gathered data provides insights into usability, user experience, and acceptance of IURO and allowed us deriving recommendations for the development of other socially interactive robots.

Affect misattribution procedure: an implicit technique to measure user experience in hri

This paper suggests new methodology for measuring User Experience in HRI. We suggest using implicit attitude to predict User Experience (Affect). Therefore we show a first validation study. The study uses short videos of a robot (IURO - Interactive Urban Robot) approaching a person and asking for the. IURO either approached a walking or a standing person. We measured peoples implicit attitude towards the robot with the Affect Misattribution Procedure (AMP). The results show that a walking person being approached by the robot evolves an implicitly more negative attitude in the observing participant whereas corresponding questionnaire items showed no difference in attitude for the approach behaviour. We conclude from these results that measuring implicit attitude in HRI is valuable for the evaluation of the User Experience of a robot.

Combining Implicit and Explicit Methods for the Evaluation of an Ambient Persuasive Factory Display

Research in ambient intelligent systems faces a challenging endeavor, namely the evaluation of user experience of ambient displays. Due to the fact that ambient displays should be unobtrusive, it is hard for users to appraise them on a reflective level (i.e. interviews and questionnaires). In this paper we present a methodological approach that combines an implicit (the Affect Misattribution Procedure (AMP)) and an explicit measurement technique (questionnaire for the persuasive effect (PeQ)) to tackle this problem. We used this approach in a study of an interface (Operator Guide) that provides information to operators in a semiconductor factory. Results show that the implicit technique is better suited to assess fine attitudinal differences on how users experience the display than explicit questionnaires. However, explicit measures are valuable to gain suggestions for improvements and thus it is concluded that this method triangulation adds value for the research on ambient persuasive interfaces.

Can You Read My Face

Our paper reports about an online study on robot facial expressions. On the one hand, we performed this study to assess the quality of the current facial expressions of two robot heads. On the other hand, we aimed at developing a simple, easy-to-use methodological variation to evaluate facial expressions of robotic heads. Short movie clips of two different robot heads showing a happy, sad, surprised, and neutral facial expression were compiled into an online survey, to examine how people interpret these expressions. Additionally, we added a control condition with a human face showing the same four emotions. The results showed that the facial expressions could be recognized well for both heads. Even the blender emotion surprised was recognized, although it resulted in positive and negative connotations. These results underline the importance of the situational context to correctly interpret emotional facial expressions. Besides the expected finding that the human is perceived significantly more anthropomorphic and animate than both robot heads, the more human-like designed robot head was rated significantly higher with respect to anthropomorphism than the robot head using animal-like features. In terms of the validation procedure, we could provide evidence for a feasible two-step procedure. By assessing the participants’ dispositional empathy with a questionnaire it can be ensured that they are in general able to decode facial expressions into the corresponding emotion. In subsequence, robot facial expressions can be validated with a closed-question approach.

Impacts of multimodal feedback on efficiency of proactive information retrieval from task-related HRI

This work is a first step towards an integration of multimodality with the aim to make efficient use of both human-like, and non-human-like feedback modalities in order to optimize proactive information retrieval from task-related Human-Robot Interaction (HRI) in human environments. The presented approach combines the human-like modalities speech and emotional facialmimicry with non-humanlike modalities. The proposed non-human-like modalities are a screen displaying retrieved knowledge of the robot to the human and a pointer mounted above the robot head for pointing directions and referring to objects in shared visual space as an equivalent for arm and hand gestures. Initially, pre-interaction feedback is explored in an experiment investigating different approach behaviors in order to find socially acceptable trajectories to increase the success of interactions and thus efficiency of information retrieval. Secondly, preevaluated human-like modalities are introduced. First results of a multimodal feedback study are presented in the context of the IURO project1, where a robot asks for its way to a predefined goal location.

Exploring Challenging Environments: Contextual Research in the Car and the Factory Through an HCI Lens

Nontraditional environments offer a variety of methodological challenges when exploring cooperation under very specific contextual conditions. We understand contexts as challenging when they exhibit very specific/unique characteristics that need to be explored beyond traditional and already better-understood working/office settings. Moreover, these challenging environments are contexts in which human-human interaction mediated by computing systems and human-machine collaboration is hard to observe. In this paper, we focus on two challenging environments: the highly context-dependent automotive environment and the complex context of a semiconductor factory. Both contexts offer potential in a variety of ways for novel computer-supported cooperative work research, such as driver/codriver cooperation and operator-robot cooperation. In this book chapter, two exemplary contexts “car” and “factory” will be characterized in terms of (1) research challenges posed by the context, (2) performed exploratory studies, and (3) methodological implications for the two exemplary contexts, as well as for CSCW and HCI research practices in general.