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Record W2909775365 · doi:10.1109/iros.2018.8594034

After You: Doorway Negotiation for Human-Robot and Robot-Robot Interaction

2018· article· en· W2909775365 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRobotComputer scienceHuman–robot interactionHuman–computer interactionRobot controlNegotiationSocial robotArtificial intelligenceMobile robotComputer vision

Abstract

fetched live from OpenAlex

We propose and test an autonomous robot behavior for socially-compliant navigation of doorways with both human and robot interlocutors. Building on previous work for “aggressive” interaction between robots to resolve navigation deadlocks in corridors, we demonstrate an “assertive” robot that negotiates right-of-way when faced with a human or other robot. The negotiation is implemented using only motion and common navigation sensors, without explicit message-passing. Our goal is for the correct agent to take priority, as decided both by time-efficiency and as judged subjectively by naive human participants. Our contribution is a practical method for doorway negotiation, and a study of human users' responses to a robot that appears to participate in existing social customs surrounding doors. Our method is evaluated with robot-robot experiments and a human-robot interaction study with nonexpert users.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.694

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 communication0.0000.001
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 head0.307
Teacher spread0.281 · 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

Citations23
Published2018
Admission routes2
Has abstractyes

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