Adaptive frequency differentiation: An approach to increase the transparency and performance of haptic devices
Bibliographic record
Abstract
There are many applications for which a robotic device is used to recreate the sense of touch for a physical or virtual environment. Transparency and stability are two major issues in controlling haptic devices. Transparency highly depends on the quality of state observation while the stability range is mainly affected by the time-delay and sampling frequency. The control force is calculated based on the model of the environment and usually is a function of the position and the velocity at the joints. Optical encoders are commonly used for position measurement because of their high resolution, robustness to noise, and high bandwidth. The velocity, however, is usually determined by differentiating the position data over time which can be noisy at high frequencies. This noise demotes the transparency and stability. Low-pass filters are widely used to filter the noise but they make the system slow and conservatively introduce time-delay which further limits the stability range. In this paper, the method of Adaptive Frequency Differentiation (AFD) is introduced, which operates at varying frequencies and effectively removes the noise caused by the error in position data. The AFD is optimized to operate at its best performance while maintaining the reliability of the differentiation. The output of the AFD is derived by logically interpreting the available data and does not involve iterative loops, which improves the processing time. An extension to this method allows to compute low-delay and noiseless acceleration directly from the position data. The claims of this paper are supported by simulation and experimental results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".