Design of a haptic display for interacting and sliding on deformable objects
Bibliographic record
Abstract
We present a stable and robust point-based haptic rendering methods to interact with various types of deformable elastic object, from (soft) low-stiff to (rigid) high-stiff, using control algorithms. The proposed method offers a high-fidelity 3D force reflecting haptic model to guarantee a stable sliding and force (feedback) field over the surface of polygonal-based deformable bodies with different normal stiffness in each triangle mesh. Control algorithms are examined to maintain and to improve the stability margins and achievable performances for the haptic display with force continuity. Two classes of control strategies are investigated. The first is a lead-lag (L-L) compensator designed based on classical control and the second scheme is a linear-quadratic-Gaussian (LQG) controller designed from modern control theory. Detailed comparison and evaluation of the proposed methods are presented to illustrate the performance of the haptic display when applied for deformable objects.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".