Mask-Grasp R-CNN: Simultaneous Instance Segmentation and Robotic Grasp Detection
Bibliographic record
Abstract
Autonomous robotics research has been driven by rapid advancements in deep learning architectures and the ability to use transfer learning to train networks using smaller datasets. This paper proposes a single deep convolutional neural network capable of simultaneously predicting objects in a scene, their segmentation mask, and a ranked list of the optimal grasping locations. For the first time in grasp detection, adaptive-size anchors are proposed as prior information for training. The proposed approach, named Mask-Grasp R-CNN, shows that an object detection and instance segmentation network can be easily extended for the grasp detection task without modifying any of its weights. Building on a Mask R-CNN network, the proposed approach detects grasping points at an instance level rather than at the image level. This enables Mask-Grasp R-CNN to achieve a 10% reduction in miss rate at 1 false-positive-per-image when evaluated on the Multi-Object dataset. The end goal is to integrate this system into a semi-autonomous control scheme to be used in upper-limb prosthetics.
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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".