On with the Visuomotor Function: A 6DOF Adaptive Approach for Modeling Image-Based Variations and Visual Servoing
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we proposes a visual servoing method that approximates the relation between the variations of image points and the variations of a stereo rig in Euclidian space. As with most image-based visual servoing methods, commands are expressed in the space of image features. However, instead of relating instantaneous image-based variations to instantaneous variations in Euclidian space, the visuomotor function relates arbitrary image-based variations to Euclidian transformations. The visuomotor function is approximated in real-time by using online estimation techniques. The system improves its performance with experience and is able to adapt to different configurations of the cameras or environment. Given the disparities between two sets of corresponding image points, the visuomotor function provides the Euclidian transformation the robot must execute in order to align the image coordinates.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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