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 614k | joint_torques listlengths 7 7 |
|---|---|---|
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End of preview. Expand in Data Studio
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
from datasets import load_dataset
dataset = load_dataset("elprofesoriqo/panda_domain_randomization")
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