Adaptive Teleoperation Control using Online Estimate of Operator's Arm Damping
Bibliographic record
Abstract
Indirect adaptive bilateral teleoperation controllers have been designed to provide compromise between stability and performance in the presence of time delays and uncertainties in operator and environment dynamics. So far, the controllers developed utilize online estimate of only environment impedance and they approximate the operator's arm highly time-varying dynamics with linear-time-invariant dynamic models. In this paper, a force-position teleoperation controller is implemented in which the master damping is adjusted in a dual manner based on the online estimate of operator's arm damping. To this purpose, a novel methodology for online estimation of forearm damping when arm movement is restricted to only elbow joint motion in horizontal plane is developed. The proposed scheme uses elbow joint angular position and velocity, and electromyogram signals collected from upper arm muscles in a radial basis function artificial neural network for online estimation of damping parameter. The adaptive bilateral controller has experimentally demonstrated superior performance and contact stability compared to a conventional force-position controller in the presence of communication delays
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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) | 0.000 | 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 | 0.001 |
| 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".