Average-Position Coordination for Distributed Multi-User Networked Haptic Cooperation
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Bibliographic record
Abstract
Proportional-derivative (PD) control is often used to coordinate the two copies of the virtual environment in distributed two-users networked haptic cooperation. However, a distributed PD controller designed for force interactions between two users may destabilize the haptic cooperation among multiple users because the effective coordination gain for each local copy of the virtual environment increases with the participant count. This paper proposes the average position (AP) strategy to upper bound the effective stiffness for the shared virtual object (SVO) coordination and, thus, to increase the stability of distributed multi-user haptic cooperation. The paper first motivates the AP strategy via continuous-time analysis of the autonomous dynamics of an SVO distributed among N users connected across a network with infinite bandwidth and no communication delay. We then investigate the effect of AP coordination on distributed multi-user haptic interactions over a network with limited bandwidth and constant and small communication delay via multi-rate stability and performance analyses of cooperative manipulations of an SVO by up to five operators. The paper shows that AP coordination: (1) has bounded effective coordination gain; (2) increases the stability region of distributed multi-user haptic cooperation compared to conventional PD coordination; and (3) renders less viscous SVO dynamics to operators than PD coordination. Three-users experimental manipulations of a shared virtual cube validate the analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it