A passivity criterion for sampled-data bilateral teleoperation systems
Bibliographic record
Abstract
In a bilateral teleoperation system, conditions involving open-loop model parameters and controller parameters for ensuring teleoperator passivity are useful as control design guidelines to attain maximum teleoperation transparency (due to passivity/transparency tradeoffs). By teleoperator, we mean the teleoperation system excluding the human operator and the remote environment. The rationale behind considering teleoperator passivity instead of teleoperation system stability is that, unlike the former, the latter is influenced by the dynamics of the human operator and the remote environment, which are typically uncertain, time-varying, and/or nonlinear. In this paper, a condition for the passivity of a teleoperator is found when the teleoperation controllers are implemented in the discrete-time domain. Such as new passivity analysis is necessary because discretization causes energy leaks and does not necessarily preserve passivity. We show that the passivity criterion for the sampled-data teleoperator imposes a lower bound on the robot damping and upper bounds on the control gains and the sampling time. The criterion has been verified through computer simulations as well as experimental tests involving a bilateral teleoperation system consisting of a pair of Phantom Omni robots.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".