A Control-Oriented Framework for Direct Impulse-Based Rendering of Haptic Contacts
Bibliographic record
Abstract
In many haptic applications, producing a sharp feeling of impact is crucial for high-fidelity force feedback rendering of virtual objects (VOs). Although suitable for rendering collision-rich haptic interactions, impulse-based methods are rarely used in a pure form. Instead, they are combined with penalty-based elements in different forms such as virtual couplings (VCs) and hybridization. In this paper, we first propose the direct impulse-based paradigm for rendering haptic contacts using a new sampled-data interpretation of the impact problem. Then, we cast this interpretation into a systematic framework entitled the generalized contact controller (GCC). This enables us to implement different contact rendering methods as controllers and to improve them by appropriating a wide array of analysis and design tools developed in the control field. We specifically show how to apply position and velocity corrections to the purely impulse-based contact controller for enhancing its energy and sustained contact characteristics, and how to add an anti-windup compensator (AWC) for meeting actuation limits. These propositions are validated via simulation and experiments, as well as via human perception studies. Results show the promising aspects of the proposed impulse-based methods for generating a sharper unfiltered feeling of rigid-body contacts even at low sampling rates.
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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".