Feedforward control for underactuated bipedal walking on varying compliant slopes
Bibliographic record
Abstract
In this paper, a feedforward control strategy is proposed to enable stable underactuated bipedal walking on varying compliant slopes with a known inclination angle, to handle the variation in natural environments. First, spring–damper units were employed in the horizontal and vertical directions to model the compliant ground, which is described as a rigid kinematic chain coupled with a spring–damper system. Second, a new definition of stable underactuated bipedal walking, based on walking speed, was proposed. Stable walking is achieved by adjusting the velocity of the biped’s center of mass (CoM) within limits that have been proven to allow at least one walking cycle. The proposed feedforward control strategy was based on the motion state of a robot’s CoM, using the new definition of stability and inspired by the gait characteristics of human walking on varying slopes. Speed control is realized by adjusting the displacement of the CoM with the change of slope to achieve stable walking. Finally, simulations were conducted to validate the proposed controller. The simulation results demonstrate that stable walking is achieved on varying compliant slopes by implementing the proposed control strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".