Design and Implementation of Haptic Tele-mentoring over the Internet
Bibliographic record
Abstract
Haptic Tele-mentoring refers to an educational technique in which the mentor can teach the mentee, in a hand-by-hand manner over communication networks, through the coupling of two haptic devices. Essentially, the realization of tele-mentoring relies on the efficient transmission of haptic information, such that either end of the network can sense and/or impart forces. A few of obstacles in developing the tele-mentoring applications over the internet are network delay, jitter, and packet loss. These impairments potentially affect the stability of the tele-mentoring system and degrade the feeling of guiding. This paper analyzes the design and implementation constraints of the system, as well as the simulation and experimental results. To compensate for the network latency, a novel approach based on the behaviors of the human arm trajectory is proposed to lower the overshoot so as to improve the overall system stability. The experimental results show the effectiveness of the anti-overshoot algorithm.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".