Equilibrium-point-based synergies that encode coordinates in task space: A practical method for translating functional synergies from human to musculoskeletal robot arm
Bibliographic record
Abstract
A practical method for exploiting the neuromuscular coordination in a human arm posture to control a musculoskeletal robotic arm is proposed. The method enables the low-dimensional control of multiple muscles in the arm robot without considering the differences in the size and force balance between the human and artificial muscles. The central idea is based on the assumption that the equilibrium point (EP) of an arm is always located at its endpoint position while maintaining the posture on the horizontal plane. The mathematical formulation obtained using this physical constraint leads to the EP-based synergies that encode the polar coordinates in the task space. We tested this theory by extracting EP-based synergies from electromyography activities during human posture maintenance and implementing them for a musculoskeletal robotic arm. The results suggest EP-based synergies could be functional modules of muscle mechanical impedance which is an essential primitive for human motor 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".