Performance Analysis of a Haptic Telemanipulation Task under Time Delay
Bibliographic record
Abstract
There is ample research on the effect of haptic teleoperation under delayed communication channels in terms of stability and system performance. Little attention, however, has been paid to the effect of delayed force feedback on users' task performance and whether force feedback is beneficial under significant communication delays. This paper investigates whether force feedback improves user's task performance in delayed teleoperation. We study peg-in-the-hole insertion/retraction, dexterous manipulation tasks involving high degrees of freedom and high forces at certain points during task execution. A user study involving unilateral (without force feedback), bilateral (with force feedback) and graphical feedback teleoperation under various delays is presented. We observed that for all feedback modalities, task completion times increase as delay increases. Haptic feedback helps reduce contact forces and the occurrence of large robot/environment forces. Furthermore, graphical feedback helps users maintain the lowest range of forces at the cost of higher task completion times. With users mindful of minimizing contact forces, haptic/graphical feedback causes the task to take more time than unilateral control. Therefore, when short completion times are crucial given a tolerance for larger forces, force feedback only serves to increase the time required to perform the task; thus, unilateral control may be sufficient.
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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 | 0.000 | 0.004 |
| 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 | 0.001 | 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".