The dataset viewer is not available because its heuristics could not detect any supported data files. You can try uploading some data files, or configuring the data files location manually.
OmniRetarget Dataset: Humanoid Loco-Manipulation & Scene Interaction
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 overT
timesteps. The vector is structured as follows:- Robot Pose (36D):
- Floating Base
[qw, qx, qy, qz, x, y, z]
(7D) - Joint Positions (29D)
- Floating Base
- 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.
- Robot Pose (36D):
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}
}
- Downloads last month
- 2,310