Enhancing Object Grasping Efficiency with Deep Learning and Post-processing for Multi-finger Robotic Hands
Bibliographic record
Abstract
This paper builds upon the well-established ML-based grasping technique, known as the Grasp-Rectangle (GR) method. The original GR method made two simplifying assumptions: it was designed exclusively for two-finger grippers, and it assumed that the gripper would approach objects solely from a top-down perspective on a horizontal surface. We have extended the GR method, for a multi-finger hand beyond these assumptions to (1) enable grasping from top and side views and (2) engage multiple points of contact, enhancing the algorithm’s overall performance. Our approach leverages geometric cues extracted from object images to calculate the optimal grasp pose and contact points, thereby enhancing grasp reliability. Extensive testing was conducted using a 7DOF robotic arm equipped with a 7-DOF 3-finger gripper. We achieved an accuracy of 98.6% on the Cornell Grasping Dataset with a processing time of 120 milliseconds. Furthermore, when assessing object grasping from both top and side perspectives, our algorithm delivered successful grasps at rates of 95% and 96%, respectively. These findings are rooted in a comprehensive series of tests performed across a diverse array of objects.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".