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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")
```
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