Initial dataset upload

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_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")
+```

data.parquet

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