Neural network-based teleoperation using Smith predictors
Why this work is in the frame
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Bibliographic record
Abstract
The introduction of communication channel tune delay and environment dynamic uncertainties create a significant challenge in the design of stable transparent bilateral teleoperation controllers. An early control methodology for time delayed systems, which is applicable to teleoperation systems is the use of Smith predictors. Recently a few Smith predictor based teleoperation control architectures have been proposed for 2-channel teleoperation systems in which the linear dynamics of the slave or environment are mapped at the master. This paper discusses the effectiveness of this control structure for 2-channel force-position teleoperation when applied to the nonlinear time varying dynamics of slave and environment. The proposed nonlinear predictive controller and its variations use neural networks to online estimate the dynamics of the slave and environment allowing replication of the environment contact force at the master using a similar network. The performance of the proposed architectures are evaluated on a teleoperation test-bed consisting of two planar twin-pantograph haptic devices.
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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 | 0.000 |
| 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 it