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Record W2109505924 · doi:10.1145/1228716.1228758

Robot expressionism through cartooning

2007· article· en· W2109505924 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRobotComputer scienceHuman–computer interactionSet (abstract data type)Variety (cybernetics)Exploratory researchHuman–robot interactionArtificial intelligenceProgramming languageSociology

Abstract

fetched live from OpenAlex

We present a new technique for human-robot interaction called robot expressionism through cartooning. We suggest that robots utilise cartoon-art techniques such as simplified and exaggerated facial expressions, stylised text, and icons for intuitive social interaction with humans. We discuss practical mixed reality solutions that allow robots to augment themselves or their surroundings with cartoon art content. Our effort is part of what we call robot expressionism, a conceptual approach to the design and analysis of robotic interfaces that focuses on providing intuitive insight into robotic states as well as the artistic quality of interaction. Our paper discusses a variety of ways that allow robots to use cartoon art and details a test bed design, implementation, and exploratory evaluation. We describe our test bed, Jeeves, which uses a Roomba, an iRobot vacuum cleaner robot, and a mixed-reality system as a platform for rapid prototyping of cartoon-art interfaces. Finally, we present a set of interaction content scenarios which use the Jeeves prototype: trash Roomba, the recycle police, and clean tracks, as well as initial exploratory evaluation of our approach.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.677

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)0.0010.000

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 head0.016
GPT teacher head0.238
Teacher spread0.223 · 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

Quick stats

Citations48
Published2007
Admission routes1
Has abstractyes

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