Dynamic Modeling and Slippage Analysis in Object Manipulation by Soft Fingers
Bibliographic record
Abstract
In this paper, dynamic modeling and slippage analysis of a three-link soft finger manipulating a rigid object on a horizontal surface is studied. In order to integrate the dynamics of soft tip with the finger linkage, power-law model and a linear viscous damper are used to model the elastic behavior and damping effect of soft tip respectively. Because of the enlarged contact area in the soft contact, a frictional moment can be exerted at the contact interface along with the normal and tangential forces. Furthermore, because of planar motion of object, frictional forces and moment are applied in the contact of object and ground. Therefore, friction limit surface is used as a mapping between contact forces/moment and sliding motions in both contacts. Instead of using equality and inequality equations of frictional contact conditions, a method is proposed to describe different states of the contact forces and moment by a single second-order differential equation with variable coefficients. This kind of formulation of the system dynamics facilitates the design of a controller to cancel the undesired slippage occurs between the soft tip and object during the manipulation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".