Two is not always better than one: Effects of teleoperation and haptic coupling
Bibliographic record
Abstract
Human-human dyads have been shown to out-perform individuals in a variety of movement tasks and develop specialized roles through haptic communication. Dyadic collaboration is a promising approach for teleoperated tasks that can benefit from the collaboration of multiple agents. In teleoperation, haptic communication depends on physical properties of the master and slave manipulators, as well as control parameters for position tracking and haptic feedback. We performed experiments to compare the performance of dyads and individuals in a teleoperated 1-degree-of-freedom target acquisition task using the da Vinci Research Kit surgical robot platform. In order to test the role of haptic communication in the collaborative task, two modes of force feedback were implemented for the dyad trials: a strong haptic coupling between the two master manipulators that attempts to simulate a physical link, and a weak haptic coupling that relates position differences through a soft linear spring. Results showed that participants were not able to improve their performance significantly by collaboration, and role specialization was not observed. We hypothesize that this result is due to limited haptic feedback and the dynamics of the teleoperated system. However, we demonstrated that most users accommodated to their partners to some extent, and users who had similar individual performance were more likely to improve as dyads.
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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 | 0.007 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".