Performance evaluation of haptic data compression methods in teleoperation systems
Bibliographic record
Abstract
In this paper, we present the performance evaluation of haptic compression methods for networked teleoperation systems or haptic interfaces in virtual environments. Haptic data, which include position, velocity, and force data exchanged through the communication channel, are considered by various compression methods based on down-sampling. We introduce the operational rate-distortion performance measure that evaluates not only the compression ratio, but also the quality of reconstructed haptic data. The deadband method, also known as a perception-based haptic compression method, is categorized as an adaptive down-sampling method and compared with a fixed rate down-sampling method. In the case of force data compression, we propose the modified deadband method, which adopts a force predictor, a quantizer, and a contact force detector. Experiments are performed by using a haptic device incorporated with a virtual teleoperator. The performance evaluation and comparison of the fixed rate down-sampling, deadband, prediction-based deadband, and modified deadband methods are provided. The results show that the proposed method achieves improvement in the compression ratio and quality measurement compared to other methods.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".