Add README

README.md

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+---
+license: mit
+language: en
+tags:
+- robotics
+- motion-retargeting
+- reinforcement-learning
+- humanoid
+- trajectory-optimization
+---
+
+# 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.
+
+<div align="center">
+<video autoplay loop muted controls width="70%">
+  <source src="https://huggingface.co/datasets/omniretarget/OmniRetarget_Dataset/resolve/main/assets/teaser.mp4" type="video/mp4">
+</video>
+</div>
+
+## Dataset Structure
+
+| Subset                   | Description                                         | Source Data     | Duration (hours) |
+| ------------------------ | --------------------------------------------------- | --------------- | ---------------- |
+| `robot-object/`          | Motions of the robot carrying objects. | OMOMO           | 2.78             |
+| `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** |                                                     |                 | **3.78** |
+
+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
+```bash
+# Clone the repository
+git lfs install
+git clone https://huggingface.co/datasets/omniretarget/OmniRetarget_Dataset
+
+# Load data
+pip install numpy
+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.
+```bash
+# Install dependencies
+pip install drake
+
+# Set `task` inside the script: "object" | "terrain" | "object-terrain"
+python visualize.py
+```
+
+## Citation
+```bibtex
+@inproceedings{Yang2026OmniRetarget,
+  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={2026}
+}
+```

assets/teaser.mp4

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+oid sha256:866b14534f597b0e2e3230518523b90f4069d2b6baefcf2e4998e6fa057fdf79
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