Reversible-Flow-Adaptation
Collection
Test-Time Adaptation for robotic control using Rectified Flows. • 8 items • Updated • 1
Search is not available for this dataset
seed int32 1 42 | step int32 0 2.23M | joint_torques listlengths 12 12 |
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End of preview. Expand in Data Studio
Dataset Card for quadruped_domain_randomization
Dataset Description
This dataset contains Proportional-Derivative (PD) corrective joint torques generated during Test-Time Adaptation (TTA) simulations for the Quadruped 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: 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 ($\mu$):
[0.5391409993171692, -0.9164595007896423, 4.03103494644165, -0.5374435782432556, -0.9164064526557922, 4.03103494644165, 0.003860426601022482, 2.696013927459717, 4.032361030578613, 0.08484119921922684, 2.695657253265381, 4.032401084899902] - Standard Deviation ($\sigma$):
[0.06029755249619484, 0.12463501840829849, 0.16779327392578125, 0.05818825960159302, 0.12457720190286636, 0.16778984665870667, 0.018216369673609734, 0.16376766562461853, 0.17438504099845886, 0.030601857230067253, 0.16376431286334991, 0.17441211640834808]
Usage
from datasets import load_dataset
dataset = load_dataset("elprofesoriqo/quadruped_domain_randomization")
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