Frequency separation in projection-based force reflection algorithms for bilateral teleoperators
Bibliographic record
Abstract
The projection-based force reflection (PBFR) algorithms were previously demonstrated to substantially improve stability characteristics of the force reflecting teleoperator systems and haptic interfaces without transparency deterioration in the steady state; however, the transient response of the PFBR algorithms suffers from relatively slow force convergence. In particular, the high frequency component of the contact force, which is very important for the haptic perception of stiff surfaces, is typically filtered out. In this paper, a solution to this problem is proposed which is based on the idea to separate different frequency bands in the force reflection signal and consequently apply the projection-based principle to the low-frequency component, while reflecting the high-frequency component directly. It is shown that, for bilateral teleoperators with irregular communication delays, stability can always be achieved by implementing the above described force reflection scheme, if the cut-off frequency of the complementary filters is sufficiently high and a certain weighting coefficient in the force reflection algorithm is sufficiently low. Experimental results demonstrate that substantial simultaneous improvement of stability and transparency is achieved using the proposed method.
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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 | 0.000 |
| 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".