Grasp evaluation method for applying static loads leading to beam failure
Bibliographic record
Abstract
This paper deals with the problem of purposefully failing or yielding an object by a robotic gripper. We propose a grasp quality measure fabricated for robotic harvesting in which picking a crop from its stem is desired. The proposed metric characterizes a suitable grasp configuration for systematically controlling the failure behavior of an object to break it at the desired location while avoiding damage on other areas. Our approach is based on failure task information and gripper wrench insertion capability. Failure task definition is accomplished using failure theories. Gripper wrench insertion capability is formulated by modeling the friction between the object and gripper. A new method inspired by human pre-manipulation process is introduced to utilize gripper itself as a friction measurement device. The provided friction model is capable of handling the anisotropic behavior of materials which is the case for fruits and vegetables. The evaluation method is formulated as a quasistatic grasp problem. Additionally, the general case of both fully-actuated and under-actuated grippers are considered. As a validation of the proposed evaluation method, experimental results for failing parts using Kuka Light-Weight Robot IV robot are presented.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".