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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_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")
+```

data.parquet

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