Improved Zero Step Push Recovery with a Unified Reduced Order Model of Standing Balance
Bibliographic record
Abstract
Standing balance for legged robots can be achieved through regulating the center of pressure (ankle strategy), the angular momentum about the center of mass (hip strategy), and the magnitude of ground reaction force (variable height strategy). Prevalent reduced order models used to model legged robots at most only capture two of these strategies, and the contribution of the three available strategies is unclear. We propose a unified reduced order model that includes all three standing balance strategies and compared push recovery simulations of the unified model against existing balancing models using a nonlinear model predictive controller. We also developed a full body controller for a simple one legged balancing robot that tracked control from the reduced order models. For both the reduced order model and robot simulations, we found that the unified model could recover successfully from the largest pushes and yielded the smallest center of mass excursions. Between the hip and variable height strategies, the hip had the greatest effect on improving performance. Our results suggest that successful implementation of a unified reduced order model on physical robots would enable a simplified controller that takes advantage of available balancing strategies as needed to recover from larger push disturbances than feasible before.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".