Coordinating limbs and spine: (Pareto-)optimal locomotion in theory, in vivo, and in robots
Bibliographic record
Abstract
Abstract Among vertebrates, patterns of movement vary considerably, from the lateral spine-based movements of fish and salamanders to the predominantly limb-based movements of mammals. Yet, we know little about why these changes may have occurred in the course of evolution. Lizards form an interesting intermediate group where locomotion appears to be driven by both motion of their limbs and lateral spinal undulation. To understand the evolution and relative advantages of limb versus spine locomotion, we developed an empirically informed mathematical model as well as a robotic model and compared in silico predictions to in-vivo data from running and climbing lizards. Our mathematical model showed that, if limbs were allowed to grow to long lengths, movements of the spine did not enable longer strides, since spinal movements reduced the achievable range of motion of the limbs before collision. Yet, in-vivo data show lateral spine movement is widespread among a diverse group of lizards moving on level ground or climbing up and down surfaces. Our climbing robotic model was able to explain this disparity, showing that increased movement of the spine was energetically favourable, being associated with a reduced cost of transport. Our robot model also revealed that stability, as another performance criterion, decreased with increased spine and limb range of motion—detailing the trade-off between speed and stability. Overall, our robotic model found a Pareto-optimal set of strides—when considering speed, efficiency, and stability—requiring both spine and limb movement, which closely agreed with movement patterns among lizards. Thus we demonstrate how robotic models, in combination with theoretical considerations, can reveal fundamental insights into the evolution of movement strategies among a broad range of taxa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".