Input-to-State Stable Bilateral Teleoperation by Dynamic Interconnection and Damping Injection: Theory and Experiments
Bibliographic record
Abstract
In bilateral teleoperation, the human operating the master and the environment interacting with the slave are part of the force feedback loop. Yet, both have time-varying and unpredictable dynamics and are challenging to model. Conventional sidestepping of the demand for their models in the stability analysis assumes passive user and environment, and controls the master-communications-slave system to be passive too. This paper circumvents the need for user and environment models in a novel way: it regards their forces as external excitations for a semiautonomous feedback loop, which it outfits with a dynamic interconnection and damping injection controller that renders time-delay teleoperation exponentially input-to-state stable. The controller uses the position and velocity of the local robot and the delayed position transmitted from the other side to robustly synchronize the master and slave under the user and environment perturbations. Lyapunov-Krasovskii stability analysis shows that the strategy, first, can confine the position error between the master and the slave to an invariant set, and, second, can drive it exponentially to a globally attractive set. The approach has practical relevance for telemanipulation tasks with given precision requirements. Experiments with a pair of Geomagic Touch robots validate the strategy compared to state-of-the-art robust position tracking designs.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".