Pareto Optimal Control for Natural and Supernatural Motions
Why this work is in the frame
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Bibliographic record
Abstract
Optimization is a natural tool for designing natural motion control strategies. However, optimal motions can be expensive to compute. Furthermore, we are often interested in knowing an entire family of optimal motions rather than single motion. For a motion such as a jump, the solution family of interest is described by the pareto-optimal front that defines the trade-off between effort and jump height. In this paper we explore algorithms for computing a set of controllers that span the pareto-optimal front for jumping motions. Once computed, these controllers can then drive physics-based simulations in real time. We also develop supernatural jump controllers through the optimized introduction of external forces. We show that the pareto-optimal front can naturally span both natural and supernatural regimes. This allows for controllers that can naturally transition from physics-based motions to motions assisted by external forces as the task demands increase.
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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 | 0.000 | 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 it