Optimal forward twisting pike somersault without self-collision
Bibliographic record
Abstract
In acrobatic sports, twisting fast before piking allows athletes to enlarge their scoring potential. Since planning the arm and hip movements to twist fast is unintuitive, optimal control appears as a powerful and risk-free tool. To our knowledge, predictive simulations of human motion did not include self-collision avoidance constraints resulting potentially in unrealistic solutions. Our objective was to generate innovative and realistic twisting techniques for forward somersaults ending in pike position by solving an optimal control problem including non-collision constraints. Optimal techniques for one, two, or three twists before piking were generated by minimising the duration of the twisting and piking phases. The model was composed of five segments with one degree of freedom at the chest and two at the hips and shoulders. We explored local minima using a multi-start approach. Solutions were further analysed to assess the impact of non-collision constraints, the segments' contribution to twist creation, and their stability. For each desired number of twists, one relevant solution was chosen. Optimisation showed that trampolinists could attempt new acrobatics: forward triple twisting somersault ending in pike position. This research also shows that non-collision constraints strongly modify the optimal techniques without impairing significantly their performance.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".