| 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 panda_reversible_flow | |
| ## Dataset Description | |
| This dataset contains Proportional-Derivative (PD) corrective joint torques generated during Test-Time Adaptation (TTA) simulations for the **Panda** 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**: Panda | |
| * **Degrees of Freedom (DOF)**: 7 | |
| ### 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 ($\mu$)**: `[-0.0009676102199591696, -0.023507313802838326, 0.0030434136278927326, -0.05878295749425888, 0.002191409934312105, 0.03670020028948784, 0.01815200224518776]` | |
| * **Standard Deviation ($\sigma$)**: `[0.4676210284233093, 0.4455776810646057, 0.6085122227668762, 0.7007313966751099, 0.15731249749660492, 0.19476990401744843, 0.12277533859014511]` | |
| ### Usage | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset("elprofesoriqo/panda_reversible_flow") | |
| ``` | |