Goal-Directed CPG-Based Control for Tensegrity Spines with Many Degrees of Freedom Traversing Irregular Terrain
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it