Optimal Control as a Tool for Innovation in Aerial Twisting on a Trampoline
Bibliographic record
Abstract
Aerial twisting techniques are preferred by trampoline coaches for their balanced landings. As these techniques are not intuitive, computer simulation has been a relevant tool to explore a variety of techniques. Up to now, twisting somersaults were mainly simulated using arm abduction/adduction only (2D). Our objective was to explore more complex (3D) but still anatomically feasible arm techniques to find innovative and robust twisting techniques. The twist rotation was maximized in a straight backward somersault performed by a model including arm abduction/adduction with and without changes in the plane of elevation. A multi-start approach was used to find a series of locally optimal performances. Six of them were retained and their robustness was assessed by adding noise to the first half of the arm kinematics and then reoptimizing the second half of the skill. We found that aerial twist performance linearly correlates with the complexity of arm trajectory. Optimal techniques share a common strategy consisting of moving the arm in a plane formed by the twisting and angular momentum axes, termed as the best tilting plane. Overall, 3D techniques are simpler and require less effort than 2D techniques for similar twist performances. Three techniques which generate ∼3 aerial twists could be used by athletes because kinematic perturbations do not compromise the performance and the landing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".