Design of a Neural Controller for Walking of a 5-Link Planar Biped Robot via Optimization
Bibliographic record
Abstract
In this chapter, the hybrid model was used for modeling the underactuated biped walker. This model consisted of single support phase and the instantaneous impact phase. The double support phase was also assumed to be instantaneous. For controlling the robot in underactuated walking, a CPG network and a new feedback network were used. It is shown that the period of the CPG is the most important factor influencing the stability of the biped walker. Biological experiments show that humans exploit the natural frequencies of their arms, swinging pendulums at comfortable frequencies equal to the natural frequencies. Extracting and using the natural frequency of the links of the robots is a desirable property of the robot controller. According to this fact, we match the endogenous frequency of each neural oscillator with the resonant frequency of the corresponding link. In this way, swinging motion or supporting motion of legs is closer to free motion of the pendulum or the inverted pendulum in each case and the motion is more effective. It is well known in biology that the CPG network with feedback signals from body can coordinate the members of the body, but there is not yet a suitable biological model for feedback network. In this chapter, we use tonic stretch reflex model as the feedback signal at
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".