Differentiable Surrogate Models for Design and Trajectory Optimization of Auxetic Soft Robots
Bibliographic record
Abstract
Soft robot designs based on auxetic (i.e., negative Poisson's ratio) lattices could offer superior dexterity, tunable local kinematics, and morphological intelligence. However, the design and control of these structures for robotic tasks, requiring multiple states and motions, remains a challenging problem. Finite element models (FEMs) offer a promising way of predicting robot behaviour that might be used for design and control optimization. Yet, these physics-based models often have high computational cost and can not provide explicit gradient information to guide the search for optimal designs. In this paper, we abstract the physical predictions of FEMs through differentiable surrogate models and demonstrate design and trajectory optimization using a gradient-based optimizer. We compare the performance of convolutional neural networks (CNNs) and graph neural networks (GNNs) as surrogate models. We then demonstrate the use of a gradient-based optimizer to find optimal designs for a specified deformation and optimal pairs of designs and actuation inputs for a trajectory specified by waypoints. In each case, the differentiable surrogate model enables the gradient-based optimizer to discover novel designs lying outside of the training data that achieve the required motions (with an relative error ≤10% for trajectories).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".