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| 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: | |
| - 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. | |