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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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