Convolutional Residual Network for Grasp Localization
Bibliographic record
Abstract
Object grasping is an important ability for carrying out complex manipulation tasks with autonomous robotic systems. The grasp localization module plays an essential role in the success of the grasp maneuver. Generally viewed as a vision perception problem, its goal is determining regions of high graspability by interpreting light and depth information. Over the past few years, several works in Deep Learning (DL) have shown the high potential of Convolutional Neural Networks (CNNs) for solving vision-related problems. Advances in residual networks have further facilitated neural network training by improving convergence time and generalization performances with identity skip connections and residual mappings. In this paper, we investigate the use of residual networks for grasp localization. A standard residual CNN for object recognition uses a global average pooling layer prior to the fully-connected layers. Our experiments have shown that this pooling layer removes the spatial correlation in the back-propagated error signal, and this prevents the network from correctly localizing good grasp regions. We propose an architecture modification that removes this limitation. Our experiments on the Cornell task have shown that our network obtained state-of-the-art performances of 10.85% and 11.86% rectangle metric error on image-wise and object-wise splits respectively. We did not use pre-training but rather opted for on-line data augmentation for managing overfitting. In comparison to previous approach that employed off-line data augmentation, our network used 15x fewer observations, which significantly reduced training time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".