A Bio-inspired Solution for Double Support Force Distribution in Humanoid Robot Locomotion
Bibliographic record
Abstract
In locomotion, the likelihood of slipping or maintaining contact is determined by the forces applied in the environment. Therefore, it is crucial to find methods of keeping forces within friction constraints. In single support, the relationship between center of mass acceleration and forces is unique. However, in double support, it becomes a non-deterministic problem. It is often assumed that forces are distributed to minimize a certain effort criterion. An interesting alternative is to distribute the forces in a manner similar to how a human would, which could result in a more human-like gait for humanoid robots. With this in mind, we introduce the modified Twin Polynomial Method (mTPM) as a technique to accurately distribute double support forces in a human-like way. The relevant relationships used in this method are presented, and we compare its performance against the current state of the art in human gait analysis. The comparison is conducted across three different types of contacts with the environment: barefoot, socks, and shoes. We show how human data differs from a ‘typical’ robot walk and use the proposed method to generate a more human-like distribution for robots.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".