Feature-based Control of Physics-based Character Animation
Bibliographic record
Abstract
Creating controllers for physics-based characters is a long-standing open problem in animation and robotics. Such controllers would have numerous applications while potentially yielding insight into human motion. Creating controllers remains difficult: current approaches are either constrained to track motion capture data, are not robust, or provide limited control over style. This thesis presents an approach to control of physics-based characters based on high-level features of human movement, such as center-of-mass, angular momentum, and end-effector motion. Objective terms are used to control each feature, and are combined via optimization. We show how locomotion can be expressed in terms of a small number of features that control balance and end-effectors. This approach is used to build controllers for biped balancing, jumping, walking, and jogging. These controllers provide numerous benefits: human-like qualities such as arm-swing, heel-off, and hip-shoulder counter-rotation emerge automatically during walking; controllers are robust to changes in body parameters; control parameters apply to intuitive properties; and controller may be mapped onto entirely new bipeds with different topology and mass distribution, without controller modifications. Transitions between multiple types of gaits, including walking, jumping, and jogging, emerge automatically. Controllers can traverse challenging terrain while following high-level user commands at interactive rates. This approach uses no motion capture or off-line optimization process. Although we focus on the challenging case of bipedal locomotion, many other types of controllers stand to benefit from our approach.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".