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Record W3035368884 · doi:10.1007/s12369-020-00670-9

How are Your Robot Friends Doing? A Design Exploration of Graphical Techniques Supporting Awareness of Robot Team Members in Teleoperation

2020· article· en· W3035368884 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

VenueInternational Journal of Social Robotics · 2020
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRobotTeleoperationHuman–computer interactionSocial robotComputer scienceArtificial intelligenceTeleroboticsHuman–robot interactionRobot controlMobile robot

Abstract

fetched live from OpenAlex

While teleoperated robots continue to proliferate in domains including search and rescue, field exploration, or the military, human error remains a primary cause for accidents or mistakes. One challenge is that teleoperating a remote robot is cognitively taxing as the operator needs to understand the robot’s state and monitor all its sensor data. In a multi-robot team, an operator needs to additionally monitor other robots’ progress, states, notifications, errors, and so on to maintain team cohesion. We conducted a design exploration of novel graphical representations of robot team-member state, to support a person controlling one robot to maintain awareness of other robots in the team. Through a series of evaluations, we examined several design parameters (text, icon, facial expression, use of color, animation, and number of team robots), resulting in a set of guidelines for graphically representing team robot states in the remote team teleoperation.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.092
GPT teacher head0.403
Teacher spread0.311 · 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