Anticipatory balance control and dimension reduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A hallmark of many skilled motions is the anticipatory nature of the balance‐related adjustments that happen in preparation for the expected evolution of forces during the motion. This can shape simulated and animated motions in subtle but important ways, help lend physical credence to the motion, and help signal the character's intent. In this article, we investigate how center‐of‐mass reference trajectories (CMRTs) can be learned so as to achieve anticipatory balance control with a state‐of‐the‐art reactive balancing system. This enables the design of physics‐based motion simulations that involve fast pose transitions as well as force‐based interactions with the environment, such as punches, pushes, and catching heavy objects. We also show that generating CMRTs in a reduced space may result in faster computation times for similar task motions that deal with environmental interactions. We demonstrate the results on planar human models and show that CMRTs generalize well across parameterized versions of a motion. We illustrate that they are also effective at conveying a mismatch between a character's expectations and reality, for example, thinking that an object is heavier than it is.
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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