Upload panda reversible flow dataset

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 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")
+```

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