Bibliographic record
Abstract
Enhancing the realism of the perceived contact force is a primary challenge in haptic rendering of virtual walls (VWs) and objects (VOs). For VOs, this goal directly translates into accurate rendering of not only stiffness, but also mass. The most challenging situation arises when the stiffness of the object is large, its mass is small, and sampling is slow. To address this challenge, a framework entitled high-fidelity haptic rendering (HFCR) has been developed. The HFCR framework is composed of the following three main strategies: an energy-consistent rendering of the contact force, smooth transition between contact modes, and remaining leak dissipation. The essence of all these strategies is to make the energy of the VO emulate its continuous-time counterpart. This is achieved through physically meaningful modifications in the constitutive relations to suppress artificial energy leaks. This paper reports simulation and experiments involving the one-dimensional canonical model of a VO to illustrate the HFCR framework and compare it to the existing methods. Results demonstrate the promising stability and force rendering fidelity of this framework.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".