Data-Driven Prediction of Magnetic Miniature Soft Robots States Based on Koopman Operator Theory
Bibliographic record
Abstract
Magnetic miniature soft robots exhibit great potential for development in fields such as medicine and environmental exploration due to their outstanding flexibility and adaptability to different environments. However, the motion of these robots in a magnetic field is characterized by high nonlinearity, and the interaction between soft materials and their environment introduces uncertainty, making it challenging to establish traditional kinematic models. In this paper, a data-driven method based on Koopman operator theory is implemented to enable state prediction for magnetic miniature soft robots. First, a simulation environment is created to obtain motion data of the robot. Through deep learning, the nonlinear state space is mapped to a linear Koopman space, and then the extended dynamic mode decomposition (EDMD) method is used to approximate the Koopman operator for prediction. Finally, in a validation dataset with different magnetic field inputs, the prediction results are compared with the actual data to assess the deviation. The results indicate that the proposed data-driven prediction method can effectively infer the motion trends of the robot, demonstrating higher predictive performance on state variables with lower complexity. This provides new ideas and methods for the motion analysis and control of magnetic miniature soft robots.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".