No abstract

Not getting enough sleep is detrimental to our health and productivity, yet we have difficulty to maintain consistent bedtimes. Technological solutions to this problem mostly focus on detecting sleep patterns and providing feedback on them. We felt there was an opportunity for a perspective that concentrates on one's subjective experience. We propose Snoozle, an actuated pillow that supports consistent bedtimes by inviting users to bed, and improves the sleeping experience by enhancing the feeling of co-presence. In this proposal, we present how the concept of Snoozle developed from structured brainstorms, storyboards and sketches. We discuss the actuated pillow behavior and the envisioned interaction, and we detail our next steps.

Abstract-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.

How can a social robot get physically close to the people it needs to interact with? We investigated the effect of a social gaze cue by a human-sized mobile robot on the effects of personal space invasion by that robot. In our 2 × 2 between-subject experiment, our robot would approach our participants (n = 83), with/without personal space invasion, and with/without a social gaze cue. With a questionnaire, we measured subjective perception of warmth, competence, and comfort after such an interaction. In addition, we used on-board sensors and a tracking system to measure the dynamics of social positioning behavior. While we did find significant differences in the social positioning dynamics of the participants, no such effect was found upon quantitative analysis of perception of the robot. In a subsequent inductive analysis we further investigated these results, our findings suggesting that the social cue did play a role for the participants -particularly related to their perceived safety.

What if a robot could detect when you think it got too close to you during its approach? This would allow it to correct or compensate for its social 'mistake'. It would also allow for a responsive approach, where that robot would reactively find suitable approach behavior through and during the interaction. We investigated if it is possible to automatically detect such social feedback cues in the context of a robot approaching a person.We collected a dataset in which our robot would repeatedly approach people (n=30) to verbally deliver a message. Approach distance and environmental noise were manipulated, and our participants were tracked (position and orientation of upper body and head). We evaluated their perception of the robot's behavior through questionnaires and found no single or joint effects of the manipulations. This showed that, in this case, personal differences are more important than contextual cues -thus highlighting the importance of responding to behavioral feedback. This dataset is being made publicly available as part of this publication † .On this dataset, we then trained a random forest classifier to infer people's perception of the robot's approach behavior from features generated from the response behaviors. This resulted in a set of relevant features that perform significantly better than chance for a participant-dependent classifier; which implies that the behaviors of our participants, even with our relatively limited tracking, contain interpretable information about their perception of the robot's behavior.Our findings demonstrate, for this specific context, that the observable behavior of people does indeed contain usable information about their subjective perception of a robot's behavior. As such they, together with the dataset, provide a stepping stone for future research into the automatic detection of such social feedback cues, e.g. with other or more fine-grained observations of people's behavior (such as facial expressions), with more sophisticated machine learning techniques, and/or in different contexts.