Datasets:

DAVIAN-Robotics
/

PHUMA

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, project page and GitHub repository.

Download and Setup

The dataset is provided as a compressed file. To use it:

# 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:

{
    '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:

@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}
}