Tunable Robustness: An Artificial Contact Strategy with Virtual Actuator Control for Balance
Bibliographic record
Abstract
Abstract Physically based characters have not yet received wide adoption in the entertainment industry because control remains both difficult and unreliable. Even with the incorporation of motion capture for reference, which adds believability, characters fail to be convincing in their appearance when the control is not robust. To address these issues, we propose a simple Jacobian transpose torque controller that employs virtual actuators to create a fast and reasonable tracking system for motion capture. We combine this controller with a novel approach we call the topple‐free foot strategy which conservatively applies artificial torques to the standing foot to produce a character that is capable of performing with arbitrary robustness. The system is both easy to implement and straightforward for the animator to adjust to the desired robustness, by considering the trade‐off between physical realism and stability. We showcase the benefit of our system with a wide variety of example simulations, including energetic motions with multiple support contact changes, such as capoeira, as well as an extension that highlights the approach coupled with a Simbicon controlled walker. With this work, we aim to advance the state‐of‐the‐art in the practical design for physically based characters that can employ unaltered reference motion (e.g. motion capture data) and directly adapt it to a simulated environment without the need for optimization or inverse dynamics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".