Evaluating Multimodal Feedback for Assembly Tasks in a Virtual Environment
Bibliographic record
Abstract
Operating power tools over extended periods of time can pose significant risks to humans, due to the strong forces and vibrations they impart to the limbs. Telemanipulation systems can be employed to minimize these risks, but may impede effective task performance due to the reduced sensory cues they typically convey. To address this shortcoming, we explore the benefits of augmenting these cues with the addition of audition, vibration, and force feedback, and evaluate them on users' performance in a VR mechanical assembly task employing a simulated impact wrench. Our research focuses on the utility of vibrotactile feedback, rendered as a simplified and attenuated version of the vibrations experienced while operating an actual impact wrench. We investigate whether such feedback can serve to enhance the operator's awareness of the state of the tool, as well as a proxy for the forces experienced during collisions and coupling, while operating the tool an actual impact wrench. Results from our user study comparing feedback modalities confirm that the introduction of vibrotactile, in addition to auditory feedback can significantly improve user performance as assessed by completion time. However, the addition of force feedback to these two modalities did not further improve performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".