Projection-Based Force-Reflection Algorithms With Frequency Separation for Bilateral Teleoperation
Bibliographic record
Abstract
Projection-based force-reflection (PBFR) algorithms have been previously demonstrated to substantially improve stability characteristics of bilateral teleoperators with communication delays; however, the transient response of the PBFR algorithms suffers from relatively slow force convergence. As a result, the high-frequency component of the reflected force is lost during the initial phase of contact with the environment, which has a strong negative effect on the haptic perception of the environmental stiffness and texture. In this paper, a new type of PBFR algorithms are developed which solve the aforementioned problem. The new algorithms are based on the idea to separate different frequency bands in the force-reflection signal and apply the PBFR principle to the low-frequency component, while reflecting the high-frequency component directly. Both theoretical analysis and experimental results are presented; the results obtained confirm that the new algorithms fundamentally improve force convergence without a negative effect on stability of the teleoperator system with communication delays.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".