Detecting Perceived Appropriateness of a Robot's Social Positioning Behavior from Non-Verbal Cues

Abstract

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.

Expressive/Sensitive

“…And here, too, the examples are too numerous to mention. From detecting interpersonal attraction from wearable sensors [5], to detection of subjective perception of a robot's behaviour from posture [13] Expressive\Sensitive: Challenges and opportunities Though we are yet to fully capitalize on the possibilities afforded by these novel (social) sensitivity technologies, there already exist various artefacts that combine expressivity with (some) sensitivity. From the early example of robotic desk lamps that try to aim their light at what their users may need lighted [3], to social media attempting to present content that is 'best' suited to individual users.…”

“…At the same time, the (young) field of Social Signal Processing [12,9] is rapidly approaching the point where artefacts can be imbued with some capacity to reflect on the social situation they are embedded in; an artificial sensitivity to the emergent social aspects of the interaction (e.g. in the context of social navigation [13]).…”