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Record W1846723108 · doi:10.5898/jhri.4.2.reveleau

Visual Representation of Sound Sources and Interaction Forces in a Teleoperation Interface for a Mobile Robot

2015· article· en· W1846723108 on OpenAlex
Aurélien Reveleau, François Ferland, Mathieu Labbé, Dominic Létourneau, François Michaud

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

VenueJournal of Human-Robot Interaction · 2015
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTeleoperationInterface (matter)Sound (geography)Human–computer interactionRepresentation (politics)Computer scienceMobile robotRobotAcousticsArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Commercial telepresence robots provide video, audio, and proximity data to remote operators through a teleoperation user interface running on standard computing devices. As new modalities such as force sensing and sound localization are being developed and tested on advanced robotic platforms, ways to integrate such information on a teleoperation interface are required. This paper demonstrates the use of visual representations of forces and sound localization in a 3D teleoperation interface. Forces are represented using colors, size, bar graphs and arrows, while speech or ring bubbles are used to represents sound positions and types. Validation of these modalities is done with 31 participants using IRL-1/TR, a humanoid platform equipped with differential elastic actuators to provide compliance and force control of its arms and capable of sound source localization. Results suggest that visual representations of interaction force and sound source can provide appropriately useful information to remote operators.

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)0.001
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: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.139
GPT teacher head0.437
Teacher spread0.298 · 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