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_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
from datasets import load_dataset
dataset = load_dataset("elprofesoriqo/panda_reversible_flow")
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