Incremental feedback, i.e. the timely response to human behavior while it is happening, has previously been found to potentially speed up human-robot interactions, but it is unclear how people evaluate incremental robots. In this study, we show that the evaluation of incremental robot response depends on the actual success of the incremental feedback; that is, if the feedback leads to increased efficiency, people evaluate the robot as more competent and more credible. If the robot does not use incremental feedback, no interaction between evaluation and efficiency can be found. Thus, incremental feedback draws people's attention to interaction success.

In this article, we address to what extent the proverb “the sound makes the music” also applies to human-robot interaction, and whether robots could profit from using speech characteristics similar to those used by charismatic speakers like Steve Jobs. In three empirical studies, we investigate the effects of using Steve Jobs’ and Mark Zuckerberg's speech characteristics during the generation of robot speech on the robot's persuasiveness and its impressionistic evaluation. The three studies address different human-robot interaction situations, which range from online questionnaires to real-time interactions with a large service robot, yet all involve both behavioral measures and users’ assessments. The results clearly show that robots can profit from using charismatic speech.

In general, people tend to place too much trust in robotic systems, also in emergency situations. Our study attempts to discover ways of reducing this overtrust, by adding vocal warnings of error from a robot that guides participants blindfolded through a maze. The results indicate that the tested vocal warnings have no effect in reducing overtrust, but we encourage further testing of similar warnings to fully explore its potential effects.

In this paper, we draw attention to the social functions of emotional display in interaction. A review of HRI papers on emotion suggests that this perspective is rarely taken in the field, but that it is useful to account for the context- and culture-dependency of emotional expression. We show in two case studies that emotional display is expected to occur at very specific places in interaction and rather independently from general emotional states, and that different cultures have different conventions regarding emotional expression. Based on conversation analytic work and the results from our case studies, we present design recommendations which allow the implementation of specific emotional signals for different human-robot interaction situations.

Trust is an important topic in medical human-robot interaction, since patients may be more fragile than other groups of people. This paper investigates the issue of users' trust when interacting with a rehabilitation robot. In the study, we investigate participants' heart rate and perception of safety in a scenario when their arm is led by the rehabilitation robot in two types of exercises at three different velocities. The participants' heart rate are measured during each exercise and the participants are asked how safe they feel after each exercise. The results showed that velocity and type of exercise has no significant influence on the participants' heart rate, but they do have significant influence on how safe they feel. We found that increasing velocity and longer exercises negatively influence participants' perception of safety.

In this paper, we address issues of timing between robot behaviors in multimodal human-robot interaction. In particular, we study what effects sequential order and simultaneity of robot arm and body movement and verbal behavior have on the fluency of interactions. In a study with the Care-O-bot, a large service robot, in a medical measurement scenario, we compare the timing of the robot's behaviors in three between-subject conditions. The results show that the relative timing of robot behaviors has significant effects on the number of problems participants encounter, and that the robot's verbal output plays a special role because participants carry their expectations from human verbal interaction into the interactions with robots.

Programming by Demonstration (PbD) is an efficient way for non-experts to teach new skills to a robot. PbD can be carried out in different ways, for instance, by kinesthetic guidance, teleoperation or by using external controls. In this paper, we compare these three ways of controlling a robot in terms of efficiency, effectiveness (success and error rate) and usability. In an industrial assembly scenario, 51 participants carried out peg-in-hole tasks using one of the three control modalities. The results show that kinesthetic guidance produces the best results. In order to test whether the problems during teleoperation are due to the fact that users cannot, like in kinesthetic guidance, switch between control points using traditional teleoperation devices, we designed a new device that allows users to switch between controls for large and small movements. A user study with 15 participants shows that the novel teleoperation device yields almost as good results as kinesthetic guidance.

This paper investigates the trust relationship between humans and a rehabilitation robot, the RoboTrainer. We present a study in which participants let the robot guide their arms through a series of preset coordinates in a 3D space. Each participant interact with the robot twice, one time where participants hold on to the robotic arm, and a second time where participants are fixated to the robotic arm. Our findings show that in general participants did not feel more insecure when fixated to the robot. However, when the robot arm moves close to participants and enter their intimate space, or when the robot moves out into an outer position participants display significantly more signs of fear opposed to when the robot arm is in a normal position.

In this paper, we explore experimentally the possible tradeoff between gaze to the user and gaze to the path in robot approach. While some previous work indicates that gaze towards the user increases perceived safety because the user feels recognized, other work indicates that it is legibility of the robot's actions that put users at ease. If the robot does not drive up to the person in a straight line directly, the robot can either continuously look at the person and thus maintain eye contact, or indicate its path through its gaze behavior, increasing legibility. In an experiment with N=36 participants, we tested the tradeoff between legibility and eye contact. The behavioral results show that users are significantly more at ease with the robot that gazes at them than with the robot that looks where it is going, measured by the number of instances of glances away from the robot. Likewise, the participants rate the robot that looks at them continuously as more intelligent and more cooperative. Thus, participants value mutual gaze higher than legibility.

Dialog between drivers and speech-based vehicle interfaces can be used as an instrument to find out what drivers might be concerned, confused or curious about in driving simulator studies. Eliciting on-going conversation with drivers about topics that go beyond navigation, control of entertainment systems, or other traditional driving related tasks is important to getting drivers to engage with the activity in an open-ended fashion. In a structured improvisational Wizard of Oz study that took place in a highly immersive driving simulator, we engaged participant drivers (N=6) in an autonomous driving course where the vehicle spoke to drivers using computer-generated natural language speech. Using microanalyses of the drivers’ responses to the car’s utter- ances, we identify a set of topics that are expected and treated as appropriate by the participants in our study, as well as a set of topics and conversational strategies that are treated as inappropriate. We also show that it is just these unexpected, inappropriate utterances that eventually increase users’ trust in the system, make them more at ease, and raise the system’s acceptability as a communication partner.

With designers increasingly moving beyond button pushing and flat-screen interaction towards tangible and embodied interaction, techniques for user studies need to develop as well. While ethnographic video studies and ethnomethod-ological analyses are becoming standard in many interaction design projects, it remains a challenge to investigate in detail how people interact with all of their body. Analysis of full-body movement is time consuming, notation techniques are rare, and findings are difficult to share between members of a design team. In this paper we propose tangible video analysis, a method developed to engage people from different backgrounds in collaboratively analysing videos with the help of physical objects. We will present one of these tools, Action Scrabble, for analysing temporal organisation of human actions. We work with a case of skilled forklift truck driving. By backtracking our design research experiments, we will unfold how and why the tangible tool succeeds in engaging designers with varied analysis experience to collaboratively focus on human action structures -- and even find video analysis fun!