A Review: Robust Locomotion for Biped Humanoid Robots
Bibliographic record
Abstract
Abstract One of the most interesting and pressing challenges in the study on biped humanoid robots is to achieve high robustness in locomotion. This paper presents a brief overview of work and methods on robust walking and running for bipedal robots. So far, many robust walking methods have been proposed to reject terrain disturbances and impulsive force disturbances. The applications of the proposed methods to real robots improve the robustness and adaptivity of robots by large margin. Up to now, bipedal robots can traverse unknown terrains with ground variation exceeding 20% of leg length. The height of obstacles increases more than threefold compared to decades ago. With regards to unexpected external force, bipedal robots can recover the balance from sudden push not only at stationary state, but also during the walk. On the other hand, the biped running is underdeveloped compared to the robust walking. Still the highest running speed is less than 3.0 m/s, not to mention the poor robustness to large disturbances.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".