A Modern Solution for an Old Calibration Problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cameras endow a robot with a sense of vision to see the world. The data acquired from a camera is originally expressed in the camera coordinate system. However, a robot manipulator only accepts the data represented in the robot coordinate system. Under such circumstances, robotic applications that employ cameras necessitate the requirement of converting the camera-acquired data into the robot coordinate system. Since the robot coordinate system is often attached to the base of the robot, the relationship between the camera's and the robot's frames (commonly described by a homogeneous transformation matrix) is composed of two parts: the transformation matrix between the base frame and the end-effector frame of the robot manipulator; and the transformation matrix between the end-effector frame and the camera frame. As the first transformation matrix can be attained from robot kinematics, the problem of representing the camera-acquired data in the robot coordinate system boils down to the estimation of the transformation matrix between the robot's end-effector frame and the camera frame. This estimation problem is widely known as the hand-eye calibration problem since the end-effector and the camera are commonly regarded as the hand and the eye of a robot, respectively. The hand-eye calibration problem plays an important role in robotic applications as it enables the use of cameras in such applications. This problem also emerges in other applications such as visual servoing, 3D scanning systems, and other sensor calibrations.
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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