High-Fidelity Rendering of Contact With Virtual Objects
Bibliographic record
Abstract
When interacting with a virtual object (VO) through a haptic device, it is crucial to feedback a contact force to the human operator (HO) that displays the VO physical properties with high fidelity. The core challenge, here, is to expand the renderable range of these properties, including larger stiffness and smaller inertia, at the available sampling rate. To address this challenge, a framework entitled high-fidelity contact rendering (HFCR) has been developed in this paper. The framework consists of three main strategies: an energy-based rendering of the contact force, smooth transition (ST) between contact modes, and remaining leak dissipation (LD). The essence of these strategies is to make the VO emulate its continuous-time counterpart. This is achieved via physically meaningful modifications in the constitutive relations to suppress artificial energy leaks. The strategies are first developed for the one-dimensional (1D) canonical VO; then, generalization to the multivariable case is discussed. Renderability has been analyzed exploring different stability criteria within a unified approach. This leads to stability charts and identification of renderable range of properties in the presence and absence of the HO. The framework has been validated through simulation and various experiments. Results verify its promising aspects for various scenarios including sustained contact and sudden collision events with or without the HO.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".