AN ADAPTIVE FEEDFORWARD CONTROL METHOD FOR UNDER-ACTUATED BIPEDAL WALKING ON THE COMPLIANT GROUND
Bibliographic record
Abstract
Motivated by the potential use of humanoid robot in real environment, an adaptive feedforward control strategy is developed to stabilize the underactuated bipedal walking on the compliant ground.First, the robot-ground coupling dynamic system is modelled as a rigid kinematical chain coupled with a spring-damper system.Then by observing the human's gait, we find the walking speed has a direct effect upon the walking stability.In consideration of the highly complicated impact of real road surface on direct walking speed control, through analysis on laws governing the robot's walking speed and the centre-of-mass (CM) motion, we establish a parameterized equivalent rod-ground coupling model based on the robot's actual state, and identify the mapping relation between its walking speed and the CM forward moving distance of a full walking cycle (x f ) adaptively.Finally, the robot's walking is stabilized through a feedforward control over x f .The availability and adaptability of this method were validated through simulations: specific to one initial gait and three compliant conditions with different damping parameters, the walking was stabilized and the rate of stable convergence improved; specific to three initial gaits and three compliant conditions with stochastic varying damping parameters, the walking was still stabilized and its performance also improved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".