Multi-metric comparison of optimal 2D grasp planning algorithms
Why this work is in the frame
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Bibliographic record
Abstract
The planning of optimal grasps is an important problem in robotics which has been investigated by many researchers. The large number of available methods has made it difficult to discern those which plan a grasp with good overall performance, i.e., one with high strength, insensitivity to positioning errors, and ease of computation. In this paper, a new grasp planning method is introduced and compared to three existing planning methods using three such metrics. A new metric for measuring the sensitivity of a grasp to positioning errors is also introduced. Since grasp planning is much simpler in 2D, and 2D grasps are applicable to many 3D objects, the four methods involve only a 2D analysis. The methods are applied to a set of six polygonal objects, ranging from 3 sided to 74 sided, and their overall performance is compared. The benchmarking procedure is readily applicable to other grasp planning methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it