Remote dynamic proxies for wave-based peer-to-peer haptic interaction
Bibliographic record
Abstract
This paper introduces a distributed haptic control architecture that can render direct interaction between users in addition to cooperative manipulation of virtual objects. The proposed architecture integrates remote dynamic proxies and peer-to-peer wave-based communications. Remote dynamic proxies are avatars of users at peer sites with motion governed by second order dynamics laws. They render physically-based motion of the distant users in the presence of update discontinuities caused by packet transmission limitations. They also enable users to touch their far away peers directly. The remote dynamic proxies are integrated with peer-to-peer wave-based communications by using wave variable controllers to connect the distributed copies of the shared virtual object, and to connect the users to their remote dynamic proxies. The proposed distributed control architecture is compared via experiments to peer-to-peer haptic cooperation with wave variable time delay compensation. The results illustrate that remote dynamic proxies with wave-based communications: (1) improve position coherency between the distributed copies of the shared virtual object; (2) render mass more faithfully in the presence of network delay; and (3) permit users to interact with each other directly in addition to enabling them to cooperatively manipulate the shared virtual object.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".