Fitted Stratified Manipulation with Decomposed Path Planning on Submanifolds
Bibliographic record
Abstract
This paper reports a new manipulation planning method based on fitted stratified manipulation. In contrast to many other methods using searching techniques, it provides a formal framework. This framework analytically solves the trajectories of the agents in the configuration space. The key feature of the technique is that it allows desired trajectories on a special submanifold called bottom stratum while fixed contact points are being supposed. It makes possible arbitrary object and finger tip motions during the manipulation. As an important consequence, the obstacle avoidance problem becomes solvable within the frame of stratified approach. However, according to the philosophy of stratified motion planning (MP), the method is still based on the flow sequence approach, implicitly decomposing the dextrous manipulation into object manipulation and grasp adjustment. Another important feature of the algorithm is that it provides a unified method on how to find a general solution that decomposes the manipulation task into separate object manipulation and finger relocation.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".