Robust Four-Channel Teleoperation Through Hybrid Damping-Stiffness Adjustment
Bibliographic record
Abstract
This paper presents three strategies that adjust the coordination damping and stiffness of four-channel teleoperators to maintain the teleoperation stable regardless of time-varying delays in the transmission of operator and environment forces between the master and slave robots. A first strategy employs hybrid control terms that depend on position errors and local velocities. Thus, the hybrid terms simultaneously and dynamically regulate the master-slave coupling and the local damping injections. They increase the robustness of the system to perturbations caused by delayed transmission of operator and environment forces, but are singular at zero velocities. A second strategy injects additional damping around zero velocities, according to the master-slave position error. The additional damping makes the hybrid term nonsingular and eliminates chattering at zero velocities. However, it cannot synchronize the master and slave robots in the presence of large position error. This problem is addressed by a third strategy, which reduces the order of the position error in the hybrid term to guarantee the dominance of the Proportional term in coordination. Then, the two robots can be synchronized from arbitrarily large position errors. Lyapunov stability analysis and hardware-in-the-loop experimental results verify and compare these three proposed hybrid approaches.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".