Introduction of the Foot Placement Estimator: A Dynamic Measure of Balance for Bipedal Robotics
Bibliographic record
Abstract
The goal of most bipedal robotics research is to develop methods of achieving a dynamically balanced gait. Most current approaches focus on maintaining the balance of the system. This paper introduces a measure called the foot placement estimator (FPE) to restore balance to an unbalanced system. We begin by developing a theoretical proof to define when a biped is stable, as well as defining the region in which stability results are valid. This forms the basis for the derivation of the FPE. The results of the FPE are then extended to a complete gait cycle using the combination of a state machine and simple linear controllers. This control system is applied to a detailed and realistic simulation based on a physical robot currently under construction. Utilizing the FPE as a measure of balance allows us to create dynamically balanced gait cycles in the presence of external disturbances, including gait initiation and termination, without any precalculated trajectories.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".