On Fitted Stratified and Semi-Stratified Geometric Manipulation Planning with Fingertip Relocations
Bibliographic record
Abstract
This paper presents two object manipulation planning methods based on fitted stratified and semi-stratified approaches using finger relocations. The problem is discussed in the framework of a motion planning problem. The goal of the methods is to steer an object from an initial configuration to a final configuration while it is possible to reposition the fingertips on the surface in a predefined way. We assume there is no rolling and sliding but finger relocations are allowed. The first technique follows a pure stratified approach, however unlike the previously published method, the exact kinematic model of the manipulation system is matched with a virtual model masking the behavior of the original system. This provides a simpler model than the earlier stratified method by reducing the generally hard symbolic computation problem to a simple (almost pure numerical) one. The paper also introduces a semi-stratified manipulation planning based on the newly defined fitted system. This second method enhances the stratified motion planning with a definition of systematic finger relocation sequence. The proposed decomposition is based on the selection of suitable reference contact points. As the main benefit, the method enables a greater freedom in defining the desired fingertip trajectories. The methods are illustrated through an example of object reorientation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".