Running with lower-body robot that mimics joint stiffness of humans
Bibliographic record
Abstract
Human running motion can be modeled using a spring-loaded inverted pendulum (SLIP), where the linear-spring-like motion of the standing leg is produced by the joint stiffness of the knee and ankle. To use running speed control in the SLIP model, we should only decide the landing placement of the leg. However, for using running speed control with a multi-joint leg, we should also decide the joint angle and joint stiffness of the standing leg because these affect the direction of the ground reaction force. In this study, we develop a running control method for a human-like multi-joint leg. To achieve a running motion, we developed a running control method including pelvis oscillation control for attaining jumping power with the joint stiffness of the leg and running speed control by changing the landing placement of the leg. For using running speed control, we estimated the ground reaction force using the equation of motion and detected the joint angles of the leg for directing the ground reaction force toward the center of mass. To evaluate the proposed control methods, we compared the estimated ground reaction force with the force measured by the real robot. Moreover, we performed a running experiment with the developed running robot. By using ground reaction force estimation, this robot could accomplish the running motion with pelvic oscillation for attaining jumping power and running speed control.
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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) | 0.000 | 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) | 0.002 | 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".