Robotic grasp detection using extreme learning machine
Bibliographic record
Abstract
Object grasping using vision is one of the important functions of manipulators. Machine learning based methods have been proposed for grasp detection. However, due to the variety of grasps and 3D shapes of objects, how to effectively find the best grasp is still a challenging issue. Thus this paper presents an extreme learning machine (ELM) based method to cope with this issue. This proposed method consists of three successive modules, including candidate object detection, estimation of object's major orientations and grasp detection. In the first module, candidate object region is extracted based on depth information. In the second module, object's major orientations guide the directions for sliding windows. In the third module, a cascaded classifier is trained to identify the right grasp. ELM is used as the base classifier in the cascade. Histograms of oriented gradients (HOG) are used as features. Experimental results in benchmark dataset and real manipulators have shown that this proposed method outperforms other methods in terms of accuracy and computational efficiency.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".