Bibliographic record
Abstract
A singularity-robust trajectory generator is presented that, given a prescribed manipulator path and corresponding kinematic solution, computes a feasible trajectory in the presence of kinematic singularities. The resulting trajectory is close to minimum time, subject to individual bounds on joint velocities and accelerations, and follows the path with precision. The algorithm has complexity O(M log M), where M is the number of robot joints, and works using “coordinate pivoting,” in which the path timing near singularities is controlled using the fastest changing joint coordinate. This allows the handling of singular situations, including linear self-motions (e.g., wrist singularities), where the speed along the path is zero but some joint velocities are nonzero. To compute the trajectory, knot points are inserted along the path, dividing it into intervals, with the knot density increasing near singularities. An appropriate path velocity is then computed at each knot point, and the resulting knot velocity sequence is integrated to yield the path timing. Examples involving the PUMA manipulator are shown.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".