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Record W4412045122 · doi:10.1145/3715668.3734184

Playing with Telepresence Robots for Design Speculation

2025· article· en· W4412045122 on OpenAlexaff
Juan Pablo Martinez Avila, Andriana Boudouraki, Harriet Cameron, Gisela Reyes-Cruz, Velvet Spors, Laia Turmo Vidal, Charles Windlin, Houda Elmimouni, Janet C. Read, Jennifer A. Rode

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Manitoba
FundersEngineering and Physical Sciences Research Council
KeywordsSpeculationRobotTeleroboticsComputer scienceHuman–computer interactionMobile robotArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

This workshop explores how telepresence robots can be used for playful design speculation, leveraging their inherent asymmetries to create engaging and innovative experiences. By focusing on playfulness instead of purely utilitarian applications this workshop seeks to transform the limitations of telepresence robots into opportunities for creative interaction. We wish to explore the ways in which we can exploit the asymmetrical capabilities of remote and local users of telepresence robots. Given the person using the robot will always have more constraints due to the technical limitations of the robot (e.g., limited movement, limited space awareness, etc), we want to investigate if moving away from utilitarian applications towards playfulness can help make these robots more attractive and useful. In this workshop participants will adapt physical games using embodied methods embodied design ideation methods, such as magic machines, embodied sketching, and soma bits, to create playful interventions with robots and to discover new ways to enhance telepresence technology. The focus is on embracing asymmetry to foster innovative, inclusive, and enjoyable interactions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.755
Threshold uncertainty score0.158

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)0.0000.000

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 head0.027
GPT teacher head0.278
Teacher spread0.251 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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