Exploring human perception-based data reduction for haptic communication in 6-DOF telepresence systems
Bibliographic record
Abstract
In this paper a human perception-based data reduction method is suggested to reduce the number of packets transmitted in 6-degrees-of-freedom (DOF) telehaptic systems; specifically in haptic-enabled telepresence. The algorithm relies on knowledge from human haptic perception in order to reduce the number of packets transmitted without compromising transparency. Several distance metrics are also discussed to best examine the acuity of human perception in detecting haptic distortion when data reduction is performed in 6-DOF settings. A validation of the haptic data reduction technique is performed in a telepresence system which enables haptic interaction with a remote virtual environment. Statistical significance tests (using Friedman's nonparametric ANOVA, and Wilcoxon-Signed Rank tests) were carried out to determine the appropriate multivariate human haptic perceptual thresholds (force, torque, orientation, etc.) required to minimize the number of packets transmitted while preserving the immersiveness of the 6-DOF telehaptic environment. It was observed that the suggested algorithm can significantly reduce haptic data traffic with little or no influence on the quality of haptic-enabled telepresence interaction.
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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 | 0.001 |
| 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".