Multimodal User Feedback During Adaptive Robot-Human Presentations
Open accessAbstract
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.
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Axelsson and Skantze (2022) found that individuals interacting with a system presenting a painting to them generally used feedback in the head and speech modalities.
“Recently, Axelsson and Skantze (2022) showed that individuals interacting with a system presenting a painting to them generally used feedback in the head and speech modalities”
From Modeling Feedback in Interaction With Conversational Agents—A Review (2022) · cites this paper as Axelsson and Skantze (2022)
Axelsson and Skantze (2022) found that feedback in the facial, body pose and gaze modalities was not important for classifying their response as positive, negative, or neutral.
“and that feedback in the facial, body pose and gaze modalities was not important for classifying their response as positive, negative, or neutral.”
From Modeling Feedback in Interaction With Conversational Agents—A Review (2022) · cites this paper as Axelsson and Skantze (2022)
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- Authors
- Axelsson, A., & Skantze, G.
- Affiliations
- KTH Royal Institute of Technology (SE)
- Corresponding author
- Axelsson, Agnes
- Skantze, Gabriel
- Venue
- Frontiers in Computer Science
- Publisher
- Frontiers Media SA
- Published
- 2022-01-03
- Volume
- 3
- ISSN
- 2624-9898 (ISSN-L)
- OpenAlex
- W4205976400
- Cited by
- 14
- References
- 101
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 1 · 2023: 3 · 2022: 1
- FWCI
- 3.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Computer Science (publishedVersion, cc-by)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Stiftelsen för Strategisk Forskning
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- multimodal feedback, human-robot interaction, adaptive presentation, museum robots, speech analysis, head gestures, facial expressions, gaze, body pose, feedback timing
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