Modular neural-visual servoing with image compression input
Why this work is in the frame
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Bibliographic record
Abstract
One of the essential problems of feature-based visual servoing is calculating the inverse Jacobian, which relates changes in features to changes in robot position. Neural networks can approximate the inverse feature Jacobian. Neural networks also allow other forms of vision input to be easily used to position the robot. The vision system is primarily responsible for reducing the dimensionality of the input to reduce the size and therefore computational load on the system. In this paper we develop a system which uses neural networks to both encode the image and generate control signals. In our system, the image dimensionality can be reduced in four ways: feature extraction, averaging compression, vector quantization, and principal component expansion. We demonstrate that it is possible to use neural networks for both image analysis and control of a vision guided robot, with little loss of accuracy when compared to feature based extraction.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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