Policy-space Interpolation for Physics-based Characters 61
Bibliographic record
Abstract
Controllers for physics-based humanoids often rely on reference animations to synthesize plausible motion trajectories for task-driven control. When the controller is a deep reinforcement learning policy, the output of several controllers can be combined to enhance controller robustness and synthesize new motion variations. However, this requires evaluating several policies at each timestep and combining their outputs, which can be computationally costly. In this work, we propose an alternative approach that combines individual controllers using linear interpolation of network parameters, thereby requiring only a single policy evaluation per timestep. Our method employs a graph-based weight regularization strategy to ensure that similar motions generate similar policy weights during training. We show that this technique produces visually indistinguishable outcomes compared to blending controller outputs, and that the approach easily integrates new control policies without retraining existing ones. We further demonstrate that interpolating or perturbing individual layers results in novel variations of the internal motion pattern that cannot be easily achieved by operating on the actions. This opens a path toward improved variability in controller networks by manipulating their weights. Several compelling use cases demonstrate the benefits of our approach, including interactive control and synthesizing motion variations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".