Deformation Modeling of Compliant Robotic Fingers Grasping Soft Objects
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In this paper, a model is shown to predict the simultaneous deformations occurring when compliant robotic fingers are grasping soft objects. This model aims at providing an accurate estimation of the penetration, internal forces, and deformed shapes of both these fingers and the objects. A particular emphasis is placed on the case when the finger is underactuated but the methodology discussed in this paper is general. Usually in the literature, underactuated fingers are modeled and designed considering their grasps of rigid object because of the complexity associated with deforming objects. This limitation severely hinders the usability of underactuated grippers and either restricts them to a narrow range of applications or requires extensive experimental testing. Furthermore, classical models of underactuated fingers in contact with objects are typically applicable with a maximum of one contact per phalanx only. The model proposed in this paper demonstrates a simple algorithm to compute a virtual subdivision of the phalanges which can be used to estimate the contact pressure arising when there are contacts at many locations simultaneously. This model also proposes a computationally efficient approximation of isotropic soft objects. Numerical simulations of the proposed model are compared here with dynamic simulations, finite element analyses, and experimental measurements which all shows its effectiveness and accuracy. Finally, the extension of the model to other types of underactuated fingers, standard grippers, and fully actuated robotic fingers as well as potential applications is discussed and illustrated.
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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) | 0.000 | 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