Neural network-based pose estimation for fixtureless assembly
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
A prototype fixtureless robotic assembly workcell will require a machine vision system to locate randomly fed parts without the use of models or camera calibration. The Feature CMAC artificial neural network has been shown to solve the 3-DOF pose estimation problem for simple target parts. In this paper, the network is extended to handle an unmodified industrial target part. A tradeoff between neural network accuracy and generalization results from the number and quality of features extracted from the image. As a result, the accuracy of Feature CMAC pose estimation is dependent on the choice of feature detection algorithm. Three such algorithms were evaluated to minimize pose estimation error. RMS errors were found to be less than 0.13 of the training interval (1.0 mm in position, and 1.2/spl deg/ in orientation), with an average worst-case grasp point error of 2.8 mm. A discussion of optical-axis bias and orientation loss is included.
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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