On Fitted Stratified and Semi-Stratified Geometric Manipulation Planning with Fingertip Relocations
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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