QUICK-PICK CNN: A NOVEL ALGORITHM FOR QUICKER DUAL-ARM GRASP LOCALISATION IN A CLUTTERED ENVIRONMENT, 1-9.
Bibliographic record
Abstract
In a progressing and complex world, robot grasping and manipulation in a cluttered environment is a challenging activity.Especially when the object to be manipulated is of unknown geometry and located in a cluttered environment.In this work, a novel quick-pick CNN(QP-CNN) algorithm is implemented to identify the best grasp for a 3D object in real time.The potential impact of this research can range from improving the speed, efficiency, and accuracy in object manipulation of unknown objects in a cluttered environment assuming a model-free context.RGB-D data from the real world about the object to be manipulated is acquired and mapped to the objects.This information acts as the input for the pre-trained networks to provide input to a 7-DOF ABB YuMi dual-arm robot.The objectwise grasping accuracy of QP-CNN is 98.1% and grasp time is 2 s with 100% reliability.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".