Nicole Mirnig

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.

Transferring human-human interaction studies to HRI scenarios in public space

This paper presents the contextual analysis of the user requirements for a mobile navigation robot in public space. Three human-human interaction studies were conducted in order to gain a holistic understanding of the public space as interaction context for itinerary requests. All three human-human requirement studies were analyzed with respect to retrieve guidelines for human-robot interaction. This empirical work should contribute by: (1) providing recommendations for a communication structure from a communication studies perspective, (2) providing recommendations for navigation principles for humanrobot interaction in public space from a socio-psychological and a HRI perspective, and (3) providing recommendations regarding (confounding) contextual variables from an HCI perspective.

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.

A communication structure for human-robot itinerary requests

To analyze the formula for success of human communication, we examined dialogs between human interactors who were asking for directions in public place and extracted those elements that are responsible for making a dialog succeed or fail. Then, we tried to rate the elements according to the grade of their influence. Based on this rating and on the Shannon & Weaver model of communication, we created a communication structure for successful human-robot communication on which further research may be based to make human-robot communication as effective as possible.

Feedback guidelines for multimodal human-robot interaction: How should a robot give feedback when asking for directions?

It is the aim of our research to explore how multimodal feedback can help a robot to carry out itinerary requests effectively and satisfactory for a human interaction partner. We conducted two studies to evaluate the feedback setup of the Interactive Urban Robot (IURO), which navigates through public space autonomously and finds its way by asking pedestrians for directions. In a Wizard-of-Oz (WOz) experiment with novice users, different feedback modalities and various combinations of them were tested against each other to ascertain the ideal setup of the robot. Subsequently, a cognitive walkthrough with HRI experts was performed to validate the results from the experiment. The results from both studies show that for itinerary requests verbal feedback is most prominent but other feedback modalities may support the conversation by providing reassurance or positive emotions.

Three Strategies for Autonomous Car-to-Pedestrian Communication: A Survival Guide

With autonomous cars being released into the wild, the question is how they integrate with conventional participants in traffic, such as pedestrians. For accident-free traffic, it is necessary for autonomous cars to interact, collaborate, and negotiate with other agents. Knowledge from social robots provides a valuable source for new interaction paradigms for autonomous cars. We raise the question of how autonomous cars communicate their intentions to pedestrians and negotiate in conflict situations. We discuss the scenario and present three possible communication strategies that are informed from human-robot interaction (HRI).

Systematic analysis of video data from different human-robot interaction studies: a categorization of social signals during error situations.

Human–robot interactions are often affected by error situations that are caused by either the robot or the human. Therefore, robots would profit from the ability to recognize when error situations occur. We investigated the verbal and non-verbal social signals that humans show when error situations occur in human–robot interaction experiments. For that, we analyzed 201 videos of five human–robot interaction user studies with varying tasks from four independent projects. The analysis shows that there are two types of error situations: social norm violations and technical failures. Social norm violations are situations in which the robot does not adhere to the underlying social script of the interaction. Technical failures are caused by technical shortcomings of the robot. The results of the video analysis show that the study participants use many head movements and very few gestures, but they often smile, when in an error situation with the robot. Another result is that the participants sometimes stop moving at the beginning of error situations. We also found that the participants talked more in the case of social norm violations and less during technical failures. Finally, the participants use fewer non-verbal social signals (for example smiling, nodding, and head shaking), when they are interacting with the robot alone and no experimenter or other human is present. The results suggest that participants do not see the robot as a social interaction partner with comparable communication skills. Our findings have implications for builders and evaluators of human–robot interaction systems. The builders need to consider including modules for recognition and classification of head movements to the robot input channels. The evaluators need to make sure that the presence of an experimenter does not skew the results of their user studies.

Face-To-Face With A Robot : What do we actually talk about?

While much of the state-of-the-art research in human-robot interaction (HRI) investigates task-oriented interaction, this paper aims at exploring what people talk about to a robot if the content of ...

A case study on the effect of feedback on itinerary requests in human-robot interaction

A previously conducted study on human-human communication in the context of itinerary requests in public space resulted in “feedback” being the most powerful influencing factor regarding the successfulness of conversations between humans. In this paper, we report on a subsequently performed Wizard-of-Oz (WOz) experiment that applied the results from the human-human study to human-robot interaction. The participants interacted with a Nao robot in a cardboard model town, where the robot asked them for directions to a specific destination. It was the aim of this experiment to validate the importance of adequately timed feedback in human-robot communication. The experiment could show that feedback is a crucial factor for successful human-robot interaction in the context of asking for directions. Adequately timed and appropriately deployed feedback fosters a vivid and natural flow of communication.

Impact of Robot Actions on Social Signals and Reaction Times in HRI Error Situations

Human-robot interaction experiments featuring error situations are often excluded from analysis. We argue that a lot of value lies hidden in this discarded data. We analyzed a corpus of 201 videos that show error situations in human-robot interaction experiments. The aim of our analysis was to research (a) if and which social signals the experiment participants show in reaction to error situations, (b) how long it takes the participants to react in the error situations, and (c) whether different robot actions elicit different social signals. We found that participants showed social signals in 49.3% of error situations, more during social norm violations and less during technical failures. Task-related actions by the robot elicited less social signals by the participants, while participants showed more social signals when the robot did not react. Finally, the participants had an overall reaction time of 1.64 seconds before they showed a social signal in response to a robot action. The reaction times are specifically long (4.39 seconds) during task-related actions that go wrong during execution.