Nonlinear and Filtered Force/Position Mappings in Bilateral Teleoperation With Application to Enhanced Stiffness Discrimination
Bibliographic record
Abstract
Motivated by applications involving soft-tissue manipulation such as robotic surgery, the transparency objectives in bilateral teleoperation are redefined to include monotonic nonlinear and linear-time-invariant filter mappings between the master/slave position and force signals. To demonstrate the utility of the new performance measures, a stiffness discrimination telemanipulation task of soft environments is considered. A nonlinear force mapping can enhance stiffness discrimination thresholds as shown through a set of psychophysics experiments. Lyapunov-based adaptive motion/force controllers are presented that can achieve the new transparency objectives in the presence of dynamic uncertainty in the master, slave, user, and environment and in the absence of time delay. Givena prioriknown bounds on unknown dynamic parameters, a framework for robust stability analysis is proposed that uses an off-axis circle criterion and the Nyquist envelope of interval plant systems. Nonlinear- and linear-filtered mappings are achieved in experiments with a two-axis teleoperation system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".