The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Storytelling Robot: Modeling and Evaluation of Human-like Gaze Behavior
Abstract
Engaging storytelling is a necessary skill for humanoid robots if they are to be used in education and entertainment applications. Storytelling requires that the humanoid robot be aware of its audience and able to direct its gaze in a natural way. In this paper, we explore how human gaze can be modeled and implemented on a humanoid robot to create a natural, human-like behavior for storytelling. Our gaze model integrates data collected from a human storyteller and a discourse structure model developed by Cassell and her colleagues for human-like conversational agents (1994). We used this model to direct the gaze of a humanoid robot, Honda's ASIMO, as he recited a Japanese fairy tale using a pre-recorded human voice. We assessed the efficacy of this gaze algorithm by manipulating the frequency of ASIMO's gaze between two participants and used pre and post questionnaires to assess whether participants evaluated the robot more positively and did better on a recall task when ASIMO looked at them more. We found that participants performed significantly better in recalling ASIMO's story when the robot looked at them more. Our results also showed significant differences in how men and women evaluated ASIMO based on the frequency of gaze they received from the robot. Our study adds to the growing evidence that there are many commonalities between human-human communication and human-robot communication
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gaze and eye contact: a research review
Kleinke, C. L. (1986) · Psychological Bulletin · doi:10.1037/0033-2909.100.1.78 · Cited by 1,455
Inference of attitudes from the posture, orientation, and distance of a communicator.
Mehrabian, A. (1968) · Journal of Consulting and Clinical Psychology · doi:10.1037/h0025906 · Cited by 259
Cited by
Social robots for education: a review.
Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., & Tanaka, F. (2018) · Science Robotics · doi:10.1126/scirobotics.aat5954 · Cited by 1,558
Kim, K., Boelling, L., Haesler, S., Bailenson, J., Bruder, G., & Welch, G. F. (2018) · 2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) · doi:10.1109/ismar.2018.00039 · Cited by 206
Robots in Groups and Teams: A Literature Review
Sebo, S., Stoll, B., Scassellati, B., & Jung, M. F. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415247 · Cited by 158
Towards human-like spoken dialogue systems
Edlund, J., Gustafson, J., Heldner, M., & Hjalmarsson, A. (2008) · Speech Communication · doi:10.1016/j.specom.2008.04.002 · Cited by 135
Turn-taking, feedback and joint attention in situated human-robot interaction
Skantze, G., Hjalmarsson, A., & Oertel, C. (2014) · Speech Communication · doi:10.1016/j.specom.2014.05.005 · Cited by 101
Metadata
- Authors
- Mutlu, B., Forlizzi, J., & Hodgins, J.
- Venue
- 2006 6th IEEE-RAS International Conference on Humanoid Robots
- Publisher
- IEEE
- Published
- 2006-12
- Pages
- 518-523
- OpenAlex
- W2543757304
- Cited by
- 348
- References
- 2
- FWCI
- 7.68
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
