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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")
```
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