Performance analysis of a manipulation task in time-delayed teleoperation
Bibliographic record
Abstract
There is ample research on the stabilization of haptic teleoperation systems under communication time delay. Little attention, however, has so far been paid to the usefulness of delayed haptic feedback on task performance. While the usefulness of haptic feedback in no-delay teleoperation has been previously established, this paper investigates whether haptic feedback helps to improve task performance in the presence of delay. We consider peg-in-the-hole insertion, which is a dexterous manipulation task requiring high force levels at certain points during the task execution. Through a user study involving unilateral and bilateral teleoperation experiments under different delays, it is observed that in both unilateral and bilateral teleoperation, the task completion time increases as delay increases. It is also seen that haptic feedback helps reduce the amount and rate of energy transfer to the environment and the occurrence of larger robot/environment interaction forces. However, with the users mindful of minimizing contact forces, haptic feedback causes the task to take more time compared to no haptic feedback regardless of the time delay. Thus, for tasks where low completion times are crucial given a tolerance for larger forces, unilateral feedback may be sufficient.
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.
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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".