Robotic Grasp Detection via Residual Efficient Channel Attention and Multiscale Feature Learning
Bibliographic record
Abstract
Current robotic grasp detection methods frequently exhibit limited accuracy due to inadequate attention to critical object features and inefficient utilization of multi-scale object information. To address these limitations, we propose a grasp detection method that systematically integrates attention mechanisms with multi-scale feature learning. A key component of our method is the Residual Efficient Channel Attention (RECA) module. By utilizing lightweight 1D convolutional operations to for cross-channel interaction and integrating residual connections to enhance feature representation, this module significantly strengthens the network’s capacity to focus on the graspable areas of objects. Furthermore, our method implements a multi-scale feature processing architecture that combines Selective Kernel (SK) convolution featured by dynamic receptive fields with frequency-domain upsampling operations, allowing the model to flexibly adjust the grasping regions in accordance with the object scales. Experimental evaluations on benchmark datasets reveal superior performance, with accuracies reaching 99.2% on the Cornell dataset and 95.9% on the Jacquard dataset. Additional assessments under multi-object scenarios further reveal that our method generates grasp representations with significantly higher grasp quality scores. Physical experiments conducted on an FR5 robotic platform confirm operational reliability, achieving 97% average success rate across 200 grasp trials, demonstrating practical efficacy in real-world applications.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".