The "feature CMAC": a neural-network-based vision system for robotic control
Bibliographic record
Abstract
A strategy for locating and grasping a target object in an unknown position using a robotic manipulator equipped with a CCD camera is described. Low-level trajectory and joint control during the grasping operation is handled by the manipulator's conventional motion controller using target-pose data provided by an artificial-neural-network-based vision system. The feature CMAC is a self-organizing neural network that efficiently transforms images of a target into estimates of its location and orientation. The approach emulates biological systems in that it begins with simple image features (e.g., corners) and successively combines them to form more complex features in order to determine object position. Knowledge of camera parameters, camera position and object models is not required since that information is incorporated into the network during a training procedure wherein the target is viewed in a series of known poses. The manipulator is used to generate the training images autonomously. No training of connection weights is required; instead, training serves only to define the network topology which requires just one pass through the training images. Experiments validating the effectiveness of the strategy on an industrial robotic workcell are presented.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".