| --- |
| 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_domain_randomization |
|
|
| ## Dataset Description |
| This dataset contains Proportional-Derivative (PD) corrective joint torques generated during Test-Time Adaptation (TTA) simulations for the **Panda** 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**: 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 ($\mu$)**: `[-0.0005189143703319132, -0.0235579926520586, 0.003010569140315056, -0.058866918087005615, 0.0021719697397202253, 0.03679513931274414, 0.018202990293502808]` |
| * **Standard Deviation ($\sigma$)**: `[0.46804776787757874, 0.44556131958961487, 0.6080793142318726, 0.7006511092185974, 0.15711797773838043, 0.1949518918991089, 0.12275472283363342]` |
| |
| ### Usage |
| ```python |
| from datasets import load_dataset |
| dataset = load_dataset("elprofesoriqo/panda_domain_randomization") |
| ``` |
| |
