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Record W4392645761 · doi:10.1145/3610978.3638150

Symbiotic Society with Avatars (SSA): Toward Empowering Social Interactions Beyond Space and Time

2024· article· en· W4392645761 on OpenAlexaff
Stela H. Seo, Daniel J. Rea, Kanae Kochigami, Takayuki Kanda, James E. Young, Yukiko Nakano, Alberto Sanfeliu, Hiroshi Ishiguro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsUniversity of ManitobaUniversity of New Brunswick
FundersMoonshot Research and Development ProgramJapan Science and Technology Agency
KeywordsAvatarRobotHuman–computer interactionComputer scienceKey (lock)Social robotArtificial intelligenceComputer securityMobile robotRobot control

Abstract

fetched live from OpenAlex

Avatar robots, representations of remote people, help them extend their physical, cognitive, and perceptual capabilities. These avatars can be a range of embodiments from virtual agents to physical robots. With avatar robots, a person (operator) can coexist in multiple locations (beyond space) by controlling multiple avatars simultaneously and move from one place to another without delays (beyond time) by connecting to another avatar in a different location. In the near future, avatar technology would change people's lives dramatically. However, we face various challenges to reach this future, including uncharted knowledge of social interactions between people and avatar robots, missing standardization on developing robots (robot specs and controls), the lack of related laws and ethical rules, and technical difficulties. With this developing area of human-robot interaction, new key challenges and problems, approaches, and data are rapidly emerging. This workshop would act as a key meeting point to focus this effort and discuss avatar robots and related research and form new collaborations to forge ahead toward symbiotic society with avatars.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.685
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.358
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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