---
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")
```