Datasets:

omniretarget
/

OmniRetarget_Dataset

language: en
license: mit
task_categories:
  - robotics
tags:
  - robotics
  - motion-retargeting
  - reinforcement-learning
  - humanoid
  - trajectory-optimization

OmniRetarget Dataset: Humanoid Loco-Manipulation & Scene Interaction

Paper | Project Page

This dataset contains motion trajectories of a G1 humanoid robot interacting with objects and complex terrains. It was generated by OMNIRETARGET, an interaction-preserving data generation engine that produces high-quality, kinematically feasible trajectories free of common artifacts like foot-skating and penetration.

Dataset Structure

Due to licensing restrictions, we cannot release the retargeted LAFAN1 dataset. However, we will open-source our retargeting code so that users can retarget the data themselves.

Subset	Description	Source Data	Duration (hours)
`robot-object/`	Motions of the robot carrying objects.	OMOMO	3.0
`robot-terrain/`	Dynamic motions of the robot climbing challenging terrains.	In-house MoCap	0.5
`robot-object-terrain/`	Motions involving both object and terrain interaction.	In-house MoCap	0.5
Total			4.0

Additionally, the models/ directory contains all the necessary URDF, SDF, and OBJ assets for visualization. These are not required for loading or training with the trajectory data.

Data Format

Each .npz file contains a single trajectory with two keys:

fps: Frames per second.
qpos: A NumPy array of shape [T, D] representing the system state over T timesteps. The vector is structured as follows:
- Robot Pose (36D):
  - Floating Base [qw, qx, qy, qz, x, y, z] (7D)
  - Joint Positions (29D)
- Object Pose (7D, optional):
  - [qw, qx, qy, qz, x, y, z]
- The total dimension D is 36 for motions without an object, and 43 with an object.

Quick Usage

# Clone the repository, install dependencies
git lfs install
git clone https://huggingface.co/datasets/omniretarget/OmniRetarget_Dataset
pip install numpy

# Load data
import glob, numpy as np
paths = glob.glob("robot-object/*.npz")
with np.load(paths[0]) as data:
    qpos = data["qpos"]  # (T, D)
    fps = float(data["fps"])  # e.g., 30.0

Visualize (optional)

A visualize.py script using Drake and Meshcat is provided.

# Install dependencies
pip install drake

# Set `task` inside the script: "object" | "terrain" | "object-terrain"
python visualize.py

Citation

https://omniretarget.github.io/

@inproceedings{Yang2025OmniRetarget,
  title={OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction},
  author={Yang, Lujie and Huang, Xiaoyu and Wu, Zhen and Kanazawa, Angjoo and Abbeel, Pieter and Sferrazza, Carmelo and Liu, C. Karen and Duan, Rocky and Shi, Guanya},
  booktitle={arXiv},
  year={2025}
}