Automated Gripper Jaw Design and Grasp Planning for Sets of 3D Objects
Bibliographic record
Abstract
Abstract An algorithm for automatically generating a common jaw design and planning grasps for a given set of polyhedral objects is presented. The algorithm is suitable for a parallel‐jaw gripper equipped with three cylindrical fingers. The common jaw design eliminates the need for custom made grippers and tool changing. The proposed jaw configuration and planning approach reduces the search associated with locating the finger contacts from six degrees‐of‐freedom to one degree‐of‐freedom. Closed‐form algorithms for checking force closure and for predicting jamming are developed. Three quality metrics are introduced to improve the quality of the planned grasps. The first is a measure of the sensitivity of the grasp to errors between the actual and planned finger locations. The second is a measure of the efficiency of the grasp in terms of the contact forces. The third is a measure of the dependence of force closure on friction. These quality metrics are not restricted to cylindrical fingers and can be applied tonfinger grasps. Running on a standard PC, the algorithm generated a solution in less than five minutes for a set of five objects with a total of 456 triangular facets. © 2003 Wiley Periodicals, Inc.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".