To prepare for a future in which robots are more commonplace, it is important to know what robot behaviors people find socially normative. Previous work suggests that for robots to be accepted by people, the robot should adhere to the prevalent social norms, such as those related to approaching people. However, we do not expect that socially normative approach behaviors for robots can be translated on a one-on-one basis from people to robots, because currently robots have unique and different features to humans, including (but not limited to) wheels, sounds, and shapes. The two studies presented in this article go beyond the state-of-the-art and focus on socially normative approach behaviors for robots. In the first study, we compared people’s responses to violations of personal space done by robots compared to people. In the second study, we explored what features (sound, size, speed) of a robot approaching people have an effect on acceptance. Findings indicate that people are more lenient toward violations of a social norm by a robot as compared to a person. Also, we found that robots can use their unique features to mitigate the negative effects of norm violations by communicating intent.

Many researchers have started to explore natural interaction scenarios for children. No matter if these children are normally developing or have special needs, evaluating Child-Robot Interaction (CRI) is a challenge. To find methods that work well and provide reliable data is difficult, for example because commonly used methods such as questionnaires do not work well particularly with younger children. Previous research has shown that children need support in expressing how they feel about technology. Given this, researchers often choose time-consuming behavioral measures from observations to evaluate CRI. However, these are not necessarily comparable between studies and robots.

Since children (5-9 years old) are still developing their emotional and social skills, their social interactional behaviors in small groups might differ from adults' interactional behaviors. In order to develop a robot that is able to support children performing collaborative tasks in small groups, it is necessary to gain a better understanding of how children interact with each other. We were interested in investigating vocal turn-taking patterns as we expect these to reveal relations to collaborative and conflict behaviors, especially with children behaviors as previous literature suggests. To that end, we collected an audiovisual corpus of children performing collaborative tasks together in groups of three. Through automatic turn-taking analyses, our results showed that speaker changes with overlaps are more common than without overlaps and children seemed to show smoother turn-taking patterns, i.e., less frequent and longer lasting speaker changes, during collaborative than conflict behaviors.

Robot users that receive psychological or psy-chotherapeutic support from robots (e.g. robots that motivate users to perform certain tasks) are usually aware of participating in a psychological intervention. The present paper aims to ascertain whether robot users should indeed remain aware, or rather unaware, of participating in such type of interventions. We present an experiment with two conditions. In one condition (direct) the robot made participants aware of being subjected to a psychological intervention, the three good things exercise from positive psychology, whereas in the other condition (indirect) participants were not made aware of the intervention. Our results show that the robot succeeded in improving participants' positive affect in the direct condition but their affect worsened in the indirect condition.

When a mobile robot interacts with a group of people, it has to consider its position and orientation. We introduce a novel study aimed at generating hypotheses on suitable behavior for such social positioning, explicitly focusing on interaction with small groups of users and allowing for the temporal and social dynamics inherent in most interactions. In particular, the interactions we look at are approach, converse and retreat. In this study, groups of three participants and a telepresence robot (controlled remotely by a fourth participant) solved a task together while we collected quantitative and qualitative data, including tracking of positioning/orientation and ratings of the behaviors used. In the data we observed a variety of patterns that can be extrapolated to hypotheses using inductive reasoning. One such pattern/hypothesis is that a (telepresence) robot could pass through a group when retreating, without this affecting how comfortable that retreat is for the group members. Another is that a group will rate the position/orientation of a (telepresence) robot as more comfortable when it is aimed more at the center of that group.

Gestures are important non-verbal signals in human communication. Research with virtual agents and robots has started to add to the scientific knowledge about gestures but many questions with respect to the use of gestures in human-computer interaction are still open. This paper investigates the influence of robot gestures on the users' perceived workload and task performance (i.e. information recall) in a direction-giving task. We conducted a 2 x 2 (robot gestures vs. no robot gestures x easy vs. difficult task) experiment. The results indicate that robot gestures increased user performance and decreased perceived workload in the difficult task but not in the easy task. Thus, robot gestures are a promising means to improve human-robot interaction particularly in challenging tasks.

No abstract available.

In our previous work we introduced functional noise as a modality for robots to communicate intent [6]. In this follow-up experiment, we replicated the first study with a robot which was taller in order to find out if the same results would apply to a tall vs. a short robot. Our results show a similar trend: a robot using functional noise is perceived more positively compared with a robot that does not.

Research has been carried out on robots approaching one person [1, 3, 4]. However, further research is needed on robots approaching groups of people. In the study reported in this paper, we studied participants who were paired up for a task and assessed their perception and behaviors as they were approached by a robot from various angles. On an individual level, participants liked the frontal approaches, and they disliked being approached from the back. However, we found that the presence of a task-partner influenced participants' comfort with a robot approaching (i.e. when the robot approaches and one is standing behind the task-partner). Apart from the positioning of the individuals, the layout of the room, position of furniture and doors, also seemed to influence their experience. This pilot study was performed with a limited number of participants (N=30). However, the study offers preliminary insights into the factors that influence the choice for a robot approach direction when approaching a pair of people that are focused on a task.

Robots have recently started to leave their safety cages to be used in close vicinity to humans. This also causes changes in the nature of the tasks that robots and humans solve together, i.e., in the degree of structure of the tasks. While traditional, industrial tasks were highly structured, the new tasks often have a low level of structure. We present a user study that compares a highly and a little structured task in a text-based computer game played by human-robot teams. The results suggest that users do not only find robots useful and motivating in highly structured tasks where they depend on their help, but also in little structured tasks that they could solve on their own.

People tend to unconsciously attribute personality traits to all kinds of technology including robots. But what personality do they want robots to have? Previous research has found support for two contradicting theories: similarity attraction and complementary attraction. The similarity attraction theory implies that people prefer a robot with a similar personality to their own (e.g., an extroverted person prefers an extroverted robot). According to the complementary attraction theory, people prefer a robot's personality opposite to their own (e.g., extroverted people prefer an introverted robot). In contrast to both theories, we argue that what is considered an appropriate personality for a robot depends on the task context. In a 2×2 between-groups experiment (N=45), we found trends that indicated similarity attraction for extrovert participants when the robot was a tour guide and complementary attraction for introverted participants when the robot was a cleaner. These trends show that preferences for robot personalities may indeed depend on the context of the robot's role and the stereotype perceptions people hold for certain jobs. Robot behaviors likely need to be adapted not in complimentary or similarity to the users' personality but to the users' expectations about what kind of personality and behaviors are consistent with such a task or role.

We evaluated the effects of robot gaze behavior on interactions with multiple users in a museum-like setting. We posit that a robot needs to divide its attention between multiple users and may be able to use its gaze to ‘point’ at objects of interest. A 2 (person-oriented [only looking at participants] vs. object-oriented [also looking at artworks] gaze) × 2 (‘favored’ [looked at more] vs. ‘not favored’ [looked at less] by the robot) mixed factorial design (N=57) study was carried out in a museum-like lab setting where a robot talked about two artworks to groups of three participants. Results indicate that ‘favored’ participants did indeed pay more attention to the robot and the artworks. However, surprisingly they paid more attention when the robot did not look over to the object of interest compared to when it did give this gaze cue. The findings suggest that using an object-oriented gaze as a cue for people to look at an object may not carry across readily from person-to-person to human-robot communication. People had trouble interpreting the cue and were possibly distracted by the robot's movement.

How a robot approaches a person greatly determines the interaction that follows. This is particularly relevant when the person has never interacted with the robot before. In human communication, we exchange a multitude of multimodal signals to communicate our intent while we approach others. However, most robots do not have the capabilities to produce such signals and easily communicate their intent. In this paper we propose to communicate intent when a robot approaches a person through functional noise and approach speed. Both were manipulated in a between-subjects experiment (N=40) either slowly increasing at the start of the approach and slowly decreasing when the robot reached the human or maximized at the start and abruptly stopped at the end of the approach. We analyzed questionnaires and video data from the interaction and found that particularly functional noise that in-/decreased in volume was helpful to communicate the robot's intent but only in congruence with an in-/decreasing velocity.

Human learning strongly depends on the ability to structure the actions of teachers in order to identify relevant parts. We propose that this is also true for learning in robots. Therefore, we apply a method for multimodal action segmentation called Acoustic Packaging to a corpus of pairs of users teaching object names to a robot. Going beyond previous use cases, we analyze how the structure of human actions changes if the robot is learning quickly or slowly. Our results reveal differences between action structuring in the conditions such as longer utterances and more motion when the robot learns slowly. We also evaluate how the partners in the pair influence each other's action structuring. The results show a strong correlation between the participants in the pairs, even more so in the trials where the robot is learning slowly. We conclude that the action structuring based on Acoustic Packaging allows robots to differentiate how well the interaction with multiple users is going and is, thus, a vehicle for feedback generation.

In this paper we argue for the development of new methodological approaches to create and evaluate robots for elderly-care, which offer support for the psychological determinants of the quality of life of elderly people. Relevant determinants, such as mood, self-efficacy and happiness are discussed in this paper in relation to older people. We offer an overview of previous work on robots offering psychological support and analyse the various methodological challenges in studying the effects of motivational and psycho-therapeutic robots on elderly people's psychological well-being.

More and more robots with social capabilities like speaking or perceiving users are being developed. However, only little is known about the question if these systems are actually treated in a social manner by their users. In particular, there is a lack of data about how users treat robots when other users are present. Therefore, this paper presents an analysis of users' linguistic behavior in interaction with a social robot. It intends to answer the questions of what users' verbal behavior tells us about their perception of the system and how completing a task in a group with another user influences their behavior towards a robot. In contrast to previous work, the paper does not entirely support the hypothesis that the users' linguistic behavior is mainly determined by their preconceptions. We found that rather the interpersonal alignment between the participants is the main factor influencing the behavior in the interaction.

Human interaction is highly intuitive: we infer reactions of our opponents mainly from what we have learned in years of experience and often assume that other people have the same knowledge about certain situations, abilities, and expectations as we do. In human-robot interaction (HRI) we cannot take for granted that this is equally true since HRI is asymmetrical. In other words, robots have different abilities, knowledge, and expectations than humans. They need to react appropriately to human expectations and behaviour. With this respect, scientific advances have been made to date for applications in entertainment and service robotics that largely depend on intuitive interaction. However, HRI today is often still unnatural, slow, and unsatisfactory for the human interlocutor. Both the sensorimotor interaction with environment and interlocutor, and the social aspects of the interaction still need to be researched and improved. Therefore, this full-day workshop aims to bring together researchers from different scientific fields to discuss these crosscutting issues and to exchange views on what are the preconditions and principles of intuitive interaction.

When and how do users interrupt the interaction with a robot and turn to the experimenter? Usually it is assumed that experimenters affect the interaction negatively and should ideally not be present at all. However, in interaction situations with autonomous systems and inexperienced users this is often not possible for safety reasons. Thus, the participants indeed at times switch their focus of attention from the robot to the experimenter. Instead of seeing this as something purely negative, we argue that answering the questions of when, why and how this happens actually bears important information about the state of the interaction and the users' understanding of it. Therefore, we analyzed a study conducted in a home tour scenario with this respect and indeed discovered certain situations when the users turned away from the robot and towards the experimenter.

What users expect of a robot strongly influences their ratings of the interaction. If the robot satisfies the expectations, the users are usually pleased. If not, their experience of the interaction is negative. We as robot designers strive to design for positive interaction experiences. Therefore, users' expectations need to be taken into account. However, in this paper we argue that not all expectations are equally important. Based on a semantic differential questionnaire, we show a correlation between the perceived importance of users' expectations and the difficulty to satisfy them in interaction. The paper argues that this correlation allows to identify the most important improvements that should be made to robot systems but also to find attributes which the robot is quite good at already.

Recent developments in robotics enable advanced human-robot interaction. Especially interactions of novice users with robots are often unpredictable and, therefore, demand for novel methods for the analysis of the interaction in systemic ways. We propose Systemic Interaction Analysis (SInA) as a method to jointly analyze system level and interaction level in an integrated manner using one tool. The approach allows us to trace back patterns that deviate from prototypical interaction sequences to the distinct system components of our autonomous robot. In this paper, we exemplarily apply the method to the analysis of the follow behavior of our domestic robot BIRON. The analysis is the basis to achieve our goal of improving human-robot interaction iteratively.

This paper investigates the influence of visual appearance of social robots on judgments about their potential applications. 183 participants rated the appropriateness of thirteen categories of applications for twelve social robots in an online study. The ratings were based on videos displaying the appearance of the robot combined with basic information about the robots' general functions. The results confirmed the hypothesis that the visual appearance of robots is a significant predictor for the estimation of applications in the eye of the beholder. Furthermore, the ratings showed an attractiveness bias: robots being judged as more attractive by the users also received more positive evaluations (i.e., ldquolikingrdquo).

In everyday conversation besides speech people also communicate by means of nonverbal cues. Facial expressions are one important cue, as they can provide useful information about the conversation, for instance, whether the interlocutor seems to understand or appears to be puzzled. Similarly, in human-robot interaction facial expressions also give feedback about the interaction situation. We present a Wizard of Oz user study in an object-teaching scenario where subjects showed several objects to a robot and taught the objects' names. Afterward, the robot should term the objects correctly. In a first evaluation, we let other people watch short video sequences of this study. They decided by looking at the face of the human whether the answer of the robot was correct (unproblematic situation) or incorrect (problematic situation). We conducted the experiments under specific conditions by varying the amount of temporal and visual context information and compare the results with related experiments described in the literature.

This paper investigates the influence of feedback provided by an autonomous robot (BIRON) on users' discursive behavior. A user study is described during which users show objects to the robot. The results of the experiment indicate, that the robot's verbal feedback utterances cause the humans to adapt their own way of speaking. The changes in users' verbal behavior are due to their beliefs about the robots knowledge and abilities. In this paper they are identified and grouped. Moreover, the data implies variations in user behavior regarding gestures. Unlike speech, the robot was not able to give feedback with gestures. Due to the lack of feedback, users did not seem to have a consistent mental representation of the robot's abilities to recognize gestures. As a result, changes between different gestures are interpreted to be unconscious variations accompanying speech.

Human-robot interaction (HRI) research is here presented into social robots that have to be able to interact with inexperienced users. In the design of these robots many research findings of human-human interaction and human-computer interaction are adopted but the direct applicability of these theories is limited because a robot is different from both humans and computers. Therefore, new methods have to be developed in HRI in order to build robots that are suitable for inexperienced users. In this paper we present a video study we conducted employing our robot BIRON (Bielefeld robot companion) which is designed for use in domestic environments. Subjects watched the system during the interaction with a human and rated two different robot behaviours (extrovert and introvert). The behaviours differed regarding verbal output and person following of the robot. Aiming to improve human-robot interaction, participantspsila ratings of the behaviours were evaluated and compared.

The paper introduces an online user study on applications for social robots with 127 participants. The potential users proposed 570 application scenarios based on the appearance and functionality of four robots presented (AIBO, BARTHOC, BIRON, iCat). The items were grouped into 13 categories which are interpreted and discussed by means of four dimensions: public vs. private use, intensity of interaction, complexity of interaction model, and functional vs. human-like appearance. The interpretation lead to three classes of applications for social robots according to the degree of social interaction: (1) Specialized Applications where the robot has to perform clearly defined tasks which are delegated by a user, (2) Public Applications which are directed to the communication with many users, and (3) Individual Applications with the need of a highly elaborated social model to maintain a variety of situations with few people.