| --- |
| language: |
| - en |
| license: mit |
| tags: |
| - robotics |
| - test-time-adaptation |
| - reinforcement-learning |
| - continuous-control |
| - flow-matching |
| dataset_info: |
| features: |
| - name: seed |
| dtype: int32 |
| - name: step |
| dtype: int32 |
| - name: joint_torques |
| sequence: float32 |
| --- |
| # Dataset Card for quadruped_domain_randomization |
|
|
| ## Dataset Description |
| This dataset contains Proportional-Derivative (PD) corrective joint torques generated during Test-Time Adaptation (TTA) simulations for the **Quadruped** environment using the **domain_randomization** policy. |
| The dataset is structured for use in downstream machine learning workflows (such as training secondary diffusion or flow-matching models). |
| |
| ### Physical Environment |
| * **Robot**: Quadruped |
| * **Degrees of Freedom (DOF)**: 12 |
| |
| ### Normalization |
| The continuous `joint_torques` have been standardized globally to zero-mean and unit-variance across all seeds. |
| To un-normalize the data back to raw physical torque values, use the following constants: |
| |
| * **Mean ($\mu$)**: `[0.5391409993171692, -0.9164595007896423, 4.03103494644165, -0.5374435782432556, -0.9164064526557922, 4.03103494644165, 0.003860426601022482, 2.696013927459717, 4.032361030578613, 0.08484119921922684, 2.695657253265381, 4.032401084899902]` |
| * **Standard Deviation ($\sigma$)**: `[0.06029755249619484, 0.12463501840829849, 0.16779327392578125, 0.05818825960159302, 0.12457720190286636, 0.16778984665870667, 0.018216369673609734, 0.16376766562461853, 0.17438504099845886, 0.030601857230067253, 0.16376431286334991, 0.17441211640834808]` |
| |
| ### Usage |
| ```python |
| from datasets import load_dataset |
| dataset = load_dataset("elprofesoriqo/quadruped_domain_randomization") |
| ``` |
| |
