Bibliographic record
Abstract
This paper proposes a method for learning a hand-eye calibration and its application for visual servoing. The goal is to develop a technique that combines the strengths of existing visual servoing methods. Particularly, as in image-based visual servoing, the error is measured in the visual space while the motor command is position-based. Hence this method approximates the visuomotor function that relates variations in the visual space to variations in the motor space at a global scale. The method used for approximating the visuomotor function is derived from the field of reinforcement learning, making our hand-eye calibration autonomous, continuous and adaptable. The visuomotor function is modeled by a linear combination of polynomials, each spanning a non-mutually exclusive subset of the visual space. Each polynomial represents the utility of motor commands for the servoing task. The goal of the calibration is to approximate the parameters of these polynomials while the system interacts with its environment. Preliminary results include centering a target in the image in which the system learns the motor commands that eliminates the errors in the visual space and generalizes the result to neighboring states in the visual space, depths and motor commands.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".