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Record W4392975260 · doi:10.1016/j.procs.2024.01.122

Remarks from an experimental study on human-robot collaborative assembly

2024· article· en· W4392975260 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

VenueProcedia Computer Science · 2024
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceRobotHuman–computer interactionHuman–robot interactionSimulationArtificial intelligence

Abstract

fetched live from OpenAlex

Human robot collaboration is becoming the norm in the workplace, due to the benefits robots can bring to efficiency and production. However, this creates highly complex and dynamic workplaces that human operators need to adapt to. Industry 5.0 promotes the use of robotics and smart technologies in a more human-centric way. However, research on how operators are affected by those changes is needed to better understand how to move towards human-centricity. As such, an experimental study was designed and performed on human robot collaborative assembly. The main aim was to investigate the correlation between cognitive load and quality due to collaboration. Here, the preliminary results of the experimental study are presented in order to remark relevant states influencing work allocation. The results showcased the need for better training and more knowledge for the operators, as well as involving operators in process and workplace design. This study helps contribute knowledge on robot implementation and process design for human robot collaboration for both researchers and operations management, as it showcases the need to involve operators in those steps due to the feedback they can provide due to their experience.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.021
GPT teacher head0.304
Teacher spread0.283 · 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