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---
license: apache-2.0
task_categories:
- robotics
- reinforcement-learning
tags:
- humanoid
- locomotion
- dataset
- retargeting
- physics-simulation
size_categories:
- 100K<n<1M
---
# PHUMA: Physically-Grounded Humanoid Locomotion Dataset
This repository provides physically-grounded humanoid locomotion dataset, PHUMA.
PHUMA leverages large-scale human motion data while overcoming physical artifacts through careful data curation and physics-constrained retargeting to create a high-quality humanoid locomotion dataset.
For detailed results, implementation notes, and videos, please visit our [paper](https://arxiv.org/abs/2510.26236), [project page](https://davian-robotics.github.io/PHUMA/) and [GitHub repository](https://github.com/DAVIAN-Robotics/PHUMA).
## Download and Setup
The dataset is provided as a compressed file. To use it:
```bash
# Download data.zip from this repository
# Then extract it:
unzip data.zip
```
This will create a `data/` directory with all the motion data.
## Dataset Structure
The dataset contains retargeted data for 2 different humanoids:
```
data/
├── g1/ # Humanoid configuration g1
└── h1_2/ # Humanoid configuration h1_2
```
## Data Format
Each `.npy` file in the dataset follows a consistent structure:
```python
{
'root_trans': (num_frames, 3), # Root translation (x, y, z)
'root_ori': (num_frames, 4), # Root orientation quaternion (x, y, z, w)
'dof_pos': (num_frames, num_dof), # Degrees of freedom positions for all joints
'fps': fps # Frame rate (frames per second)
}
```
### Field Descriptions
- **root_trans**: Root joint translation in 3D space `(x, y, z)` for each frame
- **root_ori**: Root joint orientation as quaternion `(x, y, z, w)` for each frame
- **dof_pos**: Joint positions for all degrees of freedom across frames
- **fps**: Frame rate of the motion sequence
## Citation
If you find this dataset useful in your research, please cite our paper:
```bibtex
@article{lee2025phuma,
title={PHUMA: Physically-Grounded Humanoid Locomotion Dataset},
author={Kyungmin Lee and Sibeen Kim and Minho Park and Hyunseung Kim and Dongyoon Hwang and Hojoon Lee and Jaegul Choo},
journal={arXiv preprint arXiv:2510.26236},
year={2025}
}
```
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