GQ-STN: Optimizing One-Shot Grasp Detection based on Robustness\n Classifier
Bibliographic record
Abstract
Grasping is a fundamental robotic task needed for the deployment of household\nrobots or furthering warehouse automation. However, few approaches are able to\nperform grasp detection in real time (frame rate). To this effect, we present\nGrasp Quality Spatial Transformer Network (GQ-STN), a one-shot grasp detection\nnetwork. Being based on the Spatial Transformer Network (STN), it produces not\nonly a grasp configuration, but also directly outputs a depth image centered at\nthis configuration. By connecting our architecture to an externally-trained\ngrasp robustness evaluation network, we can train efficiently to satisfy a\nrobustness metric via the backpropagation of the gradient emanating from the\nevaluation network. This removes the difficulty of training detection networks\non sparsely annotated databases, a common issue in grasping. We further propose\nto use this robustness classifier to compare approaches, being more reliable\nthan the traditional rectangle metric. Our GQ-STN is able to detect robust\ngrasps on the depth images of the Dex-Net 2.0 dataset with 92.4 % accuracy in a\nsingle pass of the network. We finally demonstrate in a physical benchmark that\nour method can propose robust grasps more often than previous sampling-based\nmethods, while being more than 60 times faster.\n
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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 | 0.001 |
| 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 | 0.001 |
| 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".