--- license: other task_categories: - tabular-regression - time-series-forecasting language: - en pretty_name: Dynamics Simulation Dataset size_categories: - 1K_/ ``` where `` 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.