| --- |
| 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_reversible_flow |
|
|
| ## Dataset Description |
| This dataset contains Proportional-Derivative (PD) corrective joint torques generated during Test-Time Adaptation (TTA) simulations for the **Quadruped** environment using the **reversible_flow** 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 ($\mu$)**: `[0.5545183420181274, -0.8843424916267395, 4.045175075531006, -0.5519294738769531, -0.8843178153038025, 4.045175552368164, 0.004045533481985331, 2.6992430686950684, 4.048221111297607, 0.08398226648569107, 2.698462724685669, 4.04833459854126]` |
| * **Standard Deviation ($\sigma$)**: `[0.061799634248018265, 0.140830397605896, 0.167127788066864, 0.061556149274110794, 0.14076882600784302, 0.1671377569437027, 0.022350719198584557, 0.19783522188663483, 0.18377649784088135, 0.03169241175055504, 0.1978311985731125, 0.1838681399822235]` |
| |
| ### Usage |
| ```python |
| from datasets import load_dataset |
| dataset = load_dataset("elprofesoriqo/quadruped_reversible_flow") |
| ``` |
| |
