Author manuscript, published in "8èmes journées de l'Association Française de Réalité Virtuelle, Augmentée, Mixte et d'Interaction 3D, Laval: France (2013)" A Methodology to Assess the Acceptability of Human-Robot Collaboration Using Virtual Reality
Bibliographic record
Abstract
Robots are becoming more and more present in our everyday life: they are already used for domestic tasks, for companionship activities, and soon they will be used to assist humans and collaborate with them in their work. Human-robot collaboration has already been studied in the industry, for ergonomics and efficiency purposes, but more from a safety than from an acceptability point of view. In this work, we focused on how people perceive robots in a collaboration task and we proposed to use virtual reality as a simulation environment to test different parameters, by making users collaborate with virtual robots. A simple use case was implemented to compare different robot appearances and different robot movements. Questionnaires and physiological measures were used to assess the acceptability level of each condition with a user study. The results showed that the perception of robot movements depended on robot appearance and that a more anthropomorphic robot, both in its appearance and movements, was not necessarily better accepted by the users in a collaboration task. Finally, this preliminary use case was also the opportunity to guarantee the relevance of using such a methodology — based on virtual reality, questionnaires and physiological measures — for future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".