Picking, grasping, or scooping small objects lying on flat surfaces: A design approach
Bibliographic record
Abstract
Grasping in constrained environments is, to this day, an ongoing research topic. Objects can rarely be grasped from arbitrary directions, hence the need to study the options available to grasp them. This paper proposes a gripper capable of grasping small or thin objects that cannot be directly pinch-grasped. The focus is placed on objects that lie on hard surfaces. The proposed approach uses a quasistatic method referred to as scooping while implementing a passive thumb to compensate for manipulator positioning errors. Hence, the robot arm does not need to be moved while the gripper is grasping an object, similarly to a human hand performing a precision grasp. The design approach is presented and the main design choice, namely the use of epicyclic gear trains instead of conventional revolute joints, is explained. The implementation of the proposed approach to the gripper design is shown. We explain how parallel pinch grasps and large grasping forces are achieved even though the mechanism does not follow the usual parallelogram four-bar implementation of parallel pinch mechanisms. The experimental validation of the proposed concept is then presented by picking up a set of test objects in sequence and demonstrating some variants of the method that expand on the concept.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".