Linear Position-Based Visual Predictive Control
Why this work is in the frame
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Bibliographic record
Abstract
Although position-based visual servoing (PBVS) scheme guarantees the global asymptotic stability and unlike the image-based visual servoing (IBVS), some issues such as image singularities and camera retreat problems do not emerge within the control procedure but, its sensitivity to camera calibration errors must be addressed. This paper presents a novel positionbased visual predictive control (PVPC) method based on the internal model control (IMC) scheme to not only overcome the issues caused by camera calibration errors, but also to handle the available constraints in the visual servoing procedure. In addition, the optimized control signal puts less pressure on the actuators while the camera goes through the smoother spatial trajectory. In order to verify the functionality and efficiency of the proposed approach, some simulation results are presented .
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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