| --- |
| license: other |
| task_categories: |
| - tabular-regression |
| - time-series-forecasting |
| language: |
| - en |
| pretty_name: Dynamics Simulation Dataset |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - dynamics |
| - simulation |
| - multibody-systems |
| - natural-coordinate-method |
| - robotics |
| - time-series |
| --- |
| |
| # Dynamics Simulation Dataset |
|
|
| ## Dataset Summary |
|
|
| This dataset contains dynamics simulation trajectories computed with the Natural Coordinate Method (NCM). It is organized by benchmark case. Each case provides a training split and a test split, and each split contains multiple simulation runs with different initial conditions. |
|
|
| The dataset is intended for learning and evaluating dynamics models, trajectory prediction methods, control-aware models, and surrogate models for multibody mechanical systems. |
|
|
| ## Directory Structure |
|
|
| Each top-level directory follows this naming pattern: |
|
|
| ```text |
| <case_name>_<split>/ |
| ``` |
|
|
| where `<split>` is either `train` or `test`. |
|
|
| Each split directory contains numbered subdirectories. Each numbered subdirectory corresponds to one simulation condition with a distinct initial state. |
|
|
| ```text |
| 2p2d_train/ |
| 1/ |
| dt.csv |
| q.csv |
| u.csv |
| t.csv |
| tu.csv |
| 2/ |
| dt.csv |
| q.csv |
| u.csv |
| t.csv |
| tu.csv |
| ``` |
|
|
| All simulation subdirectories contain: |
|
|
| - `dt.csv`: time step size. |
| - `q.csv`: natural coordinate trajectory. |
| - `u.csv`: driving input at each time step. |
|
|
| Some simpler benchmark cases additionally contain: |
|
|
| - `t.csv`: minimal coordinate trajectory, such as joint angles. |
| - `tu.csv`: driving input represented in the corresponding minimal coordinates. |
|
|
| ## Benchmark Cases |
|
|
| | Case name | Description | |
| | ----------- | ------------------------------------- | |
| | `2p2d` | Two-link planar structure | |
| | `3p2d` | Three-link planar structure | |
| | `4p2d` | Four-link planar structure | |
| | `disT_2p2d` | Dissipative two-link planar structure | |
| | `3arm2d` | Three-section planar robotic arm | |
| | `7arm2d` | Seven-link planar structure | |
| | `3Sp3d` | Three-section 3D spine-like structure | |
| | `5Sp3d` | Five-section 3D spine-like structure | |
|
|
| ## Splits |
|
|
| Each benchmark case has two splits: |
|
|
| - `train`: 64 simulation runs with different initial conditions. |
| - `test`: 100 simulation runs with different initial conditions. |
|
|
| The top-level directories are: |
|
|
| ```text |
| 2p2d_train/ 2p2d_test/ |
| 3p2d_train/ 3p2d_test/ |
| 4p2d_train/ 4p2d_test/ |
| disT_2p2d_train/ disT_2p2d_test/ |
| 3arm2d_train/ 3arm2d_test/ |
| 7arm2d_train/ 7arm2d_test/ |
| 3Sp3d_train/ 3Sp3d_test/ |
| 5Sp3d_train/ 5Sp3d_test/ |
| ``` |
|
|
| ## Data Files |
|
|
| The CSV files store time-series simulation data. Rows correspond to time steps. The columns correspond to the coordinate components or input components used by the corresponding benchmark system. |
|
|
| Because the systems have different numbers of bodies, joints, and coordinates, the dimensionality of `q.csv`, `u.csv`, `t.csv`, and `tu.csv` may differ across benchmark cases. |
|
|
| ## Dataset Creation |
|
|
| The trajectories were generated from dynamics simulations computed using the Natural Coordinate Method. Each numbered run corresponds to a different initial condition for the same benchmark case and split. |
|
|
| ## Intended Use |
|
|
| This dataset may be useful for: |
|
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| - training neural dynamics models; |
| - evaluating trajectory prediction accuracy; |
| - comparing natural-coordinate and minimal-coordinate representations; |
| - studying control inputs for simulated multibody systems; |
| - benchmarking data-driven models on planar and spatial mechanical systems. |
|
|
| ## Limitations |
|
|
| The dataset contains simulated trajectories rather than real-world measurements. Model performance on this dataset may not directly transfer to physical systems without accounting for modeling assumptions, numerical integration details, sensing noise, actuation limits, and unmodeled dynamics. |
|
|
| ## Citation |
|
|
| If you use this dataset in academic work, please cite the associated project, paper, or repository when available. |
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|