Two is not always better than one: Effects of teleoperation and haptic coupling
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.
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.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it