Evaluation of genetic algorithm on grasp planning optimization for 3D object: A comparison with simulated annealing algorithm
Bibliographic record
Abstract
Grasp planning based on geometrical information of objects can be approached as an optimization problem where a hand configuration that indicates a stable grasp needs to be located in a large search space. In this paper, we study the applicability of genetic algorithm (GA) on grasp planning optimization of 3D objects. The details are given on the selection of operators and parameters. Different sampling methods in the implementation of crossover and mutation operators are tested. A quantitative analysis including the comparison with random planner and simulated annealing (SA) method is performed to evaluate the performance of the GA based planner. GraspIt! simulator [1] is used for implementing the proposed algorithm and as the test environment. Two different quality metrics are considered. The result shows that GA is a robust method in the field of grasp planning. And the GA planner outperforms the SA planner in both pre-grasp quality and stability of the final grasp.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".