Goal-Directed CPG-Based Control for Tensegrity Spines with Many Degrees of Freedom Traversing Irregular Terrain
Bibliographic record
Abstract
Abstract To further the ability of robots to achieve goals in environments with irregular terrain, we have developed a series of tensegrity spines as an abstraction of the many degrees of freedom (DOF) compliant spines seen in nature, with full six DOF between vertebrae (constrained by a tensile network). This work provides insight into control strategies for such many DOF and compliant systems, which lack the rigidly connected segments needed by traditional control. Our Central Pattern Generator (CPG)-based controller receives both proprioceptive feedback and goal-directed input. We utilize artificial neural networks to process both the feedback and the input, and only use feedback available to our analogous robotic hardware. This approach seeks to maximize the low-level competence of the control system, by combining local reflexes with structural compliance. This is, to our knowledge, the first example of a robot controlled by CPGs that is simultaneously capable of goal-directed behavior and locomotion on irregular terrain. In addition, this is the first goal-directed controller for a tensegrity robot that can transition between different terrains.
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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) | 0.001 | 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".