Multimodal User Feedback During Adaptive Robot-Human Presentations

• Feedback is an essential part of all communication, and agents communicating with humans must be able to both give and receive feedback in order to ensure mutual understanding. In this paper, we analyse multimodal feedback given by humans towards a robot that is presenting a piece of art in a shared environment, similar to a museum setting. The data analysed contains both video and audio recordings of 28 participants, and the data has been richly annotated both in terms of multimodal cues (speech, gaze, head gestures, facial expressions, and body pose), as well as the polarity of any feedback (negative, positive, or neutral). We train statistical and machine learning models on the dataset, and find that random forest models and multinomial regression models perform well on predicting the polarity of the participants' reactions. An analysis of the different modalities shows that most information is found in the participants' speech and head gestures, while much less information is found in their facial expressions, body pose and gaze. An analysis of the timing of the feedback shows that most feedback is given when the robot makes pauses (and thereby invites feedback), but that the more exact timing of the feedback does not affect its meaning.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Människa-datorinteraktion (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Human Computer Interaction (hsv//eng)

Nyckelord

feedback

presentation

agent

robot

grounding

polarity

backchannel

multimodal

återmatning

presentation

agent

robot

återkoppling

polaritet

reaktion

multimodal

Tal- och musikkommunikation

Speech and Music Communication

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)