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README.md

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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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