Enhancement of Robot Dynamics Learning by Integrating Analytical Models into Deep Neural Networks: A Data Fusion Perspective
Bibliographic record
Abstract
Precise modeling of dynamical systems can be crucial for engineering applications. Traditional analytical models often struggle when capturing real-world complexities due to challenges in system nonlinearity representation and model parameter determination. Data-driven models, such as deep neural networks (DNNs), offer better accuracy and generalization but require large quantities of high-quality data. The present article introduces a novel method called the synthesized-data neural network (SDNN), which integrates analytical models, which represent physics, with DNNs to enhance the dynamic model. The main steps of the present method are as follows. The first three degrees of freedom (DOF) of a Kinova Gen3 Lite manipulator are formulated using the Euler–Lagrange equations of motion. The experimental data are recorded from the manipulator. Simulated data from the analytical model are combined with experimental data to train the neural network. The model’s performance is evaluated using the mean squared error (MSE) in real-time experiments with the Kinova Gen3 Lite manipulator. Training datasets represent 14 trajectories, with the MSE calculated for four testing trajectories. The obtained results have led to the following conclusions. The SDNN model has shown improved performance in predicting joint torques when compared to the purely analytical model or the purely data-driven model. The SDNN, when trained with synthesized data from 14 trajectories (SDNN-14), achieved the lowest MSE of 2.14, outperforming the analytical model (MSE of 2.81) and the neural network trained solely on experimental data (MSE of 3.05).
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".