As home robot companions become more common, it is important to understand what types of tasks are considered critical to perform correctly. This paper provides working definitions of task criticality, physical and cognitive tasks with respect to robot task performance. Our research also suggests that although people's perceptions of task criticality is independent of robot appearances, their expectation that a robot performs tasks correctly is affected by it's appearance.

We present a study involving 160 participants investigating the effect of associating professional status and ethnicity with an agent by manipulating its appearance, language, and level of education. We aim to discern perceptions of status and ethnicity with respect to participants' cultural background by inviting participants from two different cultural groups (Middle Eastern and Western) to take part in our study. Results revealed that participants' cultural background had a strong impact on their ratings of the agent and its message. However, neither the agent's portrayed status nor its ethnicity appeared to have an effect on participants' perceptions of the agent. We further found that participants from both cultural backgrounds holding a negative attitude towards robots in general tend to perceive the presented message by the agent more negatively. Middle Eastern participants had a more positive attitude towards robotic agents than Western participants, which might have been the main influence on their perception of the message presented by the agent. In addition, participants who identified the agent as a member of their own cultural group perceived the presented message more positively than those from the other cultural group. We discuss our results with an intention to inform design implications for agents in a cross-cultural context.

In the late 1990s, the question of control was raised in the Human Computer Interaction (HCI) community within the process of designing computer interfaces. Following in their footsteps, the question of how much people want to be in control of their robots and how it affects the way we should design robotic interfaces is explored. To investigate the subject, we conducted a study which involved two fully autonomously operating mobile robots, namely a multi-purpose companion robot and a single-purpose domestic robot. The purpose of the study was to evaluate participants' sense of control (perceived control -who they felt was in charge of the robots- and desired control -how they wanted the action to be executed-) for a common domestic task: cleaning. Unexpectedly, the results show the higher the participants' desired control was, the more autonomous they wanted the companion robot to be (meaning the robot executed the needed task without an explicit permission from the participants).

Interacting with humans is one of the main challenges for mobile robots in a human inhabited environment. To enable adaptive behavior, a robot needs to recognize touch gestures and/or the proximity to interacting individuals. Moreover, a robot interacting with two or more humans usually needs to distinguish between them. However, this remains both a configuration and cost intensive task. In this paper we utilize inexpensive Bluetooth Low Energy (BLE) devices and propose an easy and configurable technique to enhance the robot's capabilities to interact with surrounding people. In a noisy laboratory setting, a mobile spherical robot is utilized in three proof-of-concept experiments of the proposed system architecture. Firstly, we enhance the robot with proximity information about the individuals in the surrounding environment. Secondly, we exploit BLE to utilize it as a touch sensor. And lastly, we use BLE to distinguish between interacting individuals. Results show that observing the raw received signal strength (RSS) between BLE devices already enhances the robot's interaction capabilities and that the provided infrastructure can be facilitated to enable adaptive behavior in the future. We show one and the same sensor system can be used to detect different types of information relevant in human-robot interaction (HRI) experiments.

It is essential for robots working in close proximity to people to be both safe and trustworthy. We present a case study on formal verification for a high-level planner/scheduler for the Care-O-bot, an autonomous personal robotic assistant. We describe how a model of the Care-O-bot and its environment was developed using Brahms, a multiagent workflow language. Formal verification was then carried out by automatically translating this model to the input language of an existing model checker. Four sample properties based on system requirements were verified. We then refined the environment model three times to increase its accuracy and the persuasiveness of the formal verification results. The first refinement uses a user activity log based on real-life experiments, but is deterministic. The second refinement uses the activities from the user activity log nondeterministically. The third refinement uses “conjoined activities” based on an observation that many user activities can overlap. The four samples properties were verified for each refinement of the environment model. Finally, we discuss the approach of environment model refinement with respect to this case study.

How do mistakes made by a robot affect its trustworthiness and acceptance in human-robot collaboration? We investigate how the perception of erroneous robot behavior may influence human interaction choices and the willingness to cooperate with the robot by following a number of its unusual requests. For this purpose, we conducted an experiment in which participants interacted with a home companion robot in one of two experimental conditions: (1) the correct mode or (2) the faulty mode. Our findings reveal that, while significantly affecting subjective perceptions of the robot and assessments of its reliability and trustworthiness, the robot's performance does not seem to substantially influence participants' decisions to (not) comply with its requests. However, our results further suggest that the nature of the task requested by the robot, e.g. whether its effects are revocable as opposed to irrevocable, has a significant impact on participants' willingness to follow its instructions.

How do politeness strategies and cultural aspects affect robot acceptance and anthropomorphization across native speakers of English and Arabic? Previous work in cross-cultural HRI studies has mostly focused on Western and East Asian cultures. In contrast, Middle Eastern attitudes and perceptions of robot assistants are a barely researched topic. We investigated culture-specific determinants of robot acceptance and anthropomorphization by conducting a between-subjects study in Qatar. A total of 92 native speakers of either English or Arabic interacted with a receptionist robot in two different interaction tasks. We further manipulated the robot's verbal behavior in experimental sub-groups to explore different politeness strategies. Our results suggest that Arab participants perceived the robot more positively and anthropomorphized it more than English speaking participants. In addition, the use of positive politeness strategies and the change of interaction task had an effect on participants' HRI experience. Our findings complement the existing body of cross-cultural HRI research with a Middle Eastern perspective that will help to inform the design of robots intended for use in cross-cultural, multi-lingual settings.

This full day workshop aims to bring together researchers from the embodied conversational agent (ECA) and sociable robotics communities to spark discussion and collaboration between the related fields. The focus of the workshop is on co-verbal behavior production -- specifically, synchronized speech and gesture -- for either virtually or physically embodied platforms. It elucidates the subject in consideration of aspects regarding planning and realization of multimodal behavior production. Topics discussed highlight common as well as distinguishing factors of their implementations within each respective field.

Gesture is an important feature of social interaction, frequently used by human speakers to illustrate what speech alone cannot provide, e.g. to convey referential, spatial or iconic information. Accordingly, humanoid robots that are intended to engage in natural human-robot interaction should produce speech-accompanying gestures for comprehensible and believable behavior. But how does a robot's non-verbal behavior influence human evaluation of communication quality and the robot itself? To address this research question we conducted two experimental studies. Using the Honda humanoid robot we investigated how humans perceive various gestural patterns performed by the robot as they interact in a situational context. Our findings suggest that the robot is evaluated more positively when non-verbal behaviors such as hand and arm gestures are displayed along with speech. These findings were found to be enhanced when the participants were explicitly requested to direct their attention towards the robot during the interaction.

One of the crucial aspects in building sociable, communicative robots is to endow them with expressive nonverbal behaviors. Gesture is one such behavior, frequently used by human speakers to illustrate what they express in speech. The production of gestures, however, poses a number of challenges with regard to motor control for arbitrary, expressive hand-arm movement and its coordination with other interaction modalities. We describe an approach to enable the humanoid robot ASIMO to flexibly produce communicative gestures at run-time, building upon the Articulated Communicator Engine (ACE) that was developed to allow virtual agents to realize planned behavior representations on the spot. We present a control architecture that tightly couples ACE with ASIMO's perceptuo-motor system for multi-modal scheduling. In this way, we combine conceptual representation and planning with motor control primitives for meaningful arm movements of a physical robot body. First results of realized gesture representations are presented and discussed

The generation of communicative, speech-accompanying robot gesture is still largely unexplored. We present an approach to enable the humanoid robot ASIMO to flexibly produce speech and co-verbal gestures at run-time, while not being limited to a pre-defined repertoire of motor actions. Since much research has already been dedicated to this challenge within the domain of virtual conversational agents, we build upon the experience gained from the development of a speech and gesture production model used for the virtual human Max. We propose a robot control architecture building upon the Articulated Communicator Engine (ACE) that was developed to allow virtual agents to flexibly realize planned multi-modal behavior representations on the spot. Our approach tightly couples ACE with ASIMO's perceptuo-motor system, combining conceptual representation and planning with motor control primitives for speech and arm movements of a physical robot body. First results of both gesture production and speech synthesis using ACE and the MARY text-to-speech system are presented and discussed.