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license: mit
---
<p align="center">
<h2>Dexora: Open-Source VLA for High-DoF Bimanual Dexterity</h2>
</p>
<p align="center">
<a href="#"><img src="https://img.shields.io/badge/arXiv-2026.xxxxx-B31B1B.svg" alt="arXiv"></a>
<a href="https://github.com/ZZongzheng0918/Dexora?tab=readme-ov-file"><img src="https://img.shields.io/badge/Project-Page-blue.svg" alt="Project Page"></a>
<a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License"></a>
</p>
## 🔥 News & Updates
- **2025-12-03**: Released the full **Real-World Dataset** (**10K episodes**) on Hugging Face.
--
## 📊 Dataset Overview
The Dexora corpus combines **high-fidelity real-world teleoperation data** with a **large-scale simulated dataset** designed to match the embodiment of the physical robot.
### A. Dexora Real-World Dataset (High-Fidelity)
The Dexora real-world dataset consists of **11.5K teleoperated episodes**, **2.92M frames**, and **40.5 hours** of data. Demonstrations are collected using a **hybrid teleoperation system** that couples an **Exoskeleton** (for arm control) with **Vision Pro** (for dexterous hand control), enabling precise 36-DoF bimanual manipulation on real hardware.
<p align="center">
<img src="assets/image/dataset.gif" alt="Dexora Multi-view Dataset" width="100%">
</p>
<p align="center">
<i>Video 1. <b>Synchronized Multi-View Recordings.</b> High-resolution streams from ego-centric, third-person, and wrist-mounted cameras, synchronized with 36-DoF robot proprioception.</i>
</p>
<p align="center">
<img src="assets/image/real-data.JPG" alt="Dexora Real-World Dataset Mosaic" width="100%">
</p>
<p align="center">
<i>Fig 1. <b>High-Fidelity Real-World Scenes.</b> Collected via our hybrid teleoperation system (Exoskeleton for arm + Vision Pro for hand), this dataset covers <b>347 objects</b> across diverse environments. It captures varying lighting conditions, background clutter, and precise bimanual interactions essential for robust policy learning. Panels (a–d) correspond to four task categories: <b>pick-and-place</b>, <b>assembly</b>, <b>articulation</b>, and <b>dexterous manipulation</b>.</i>
</p>
<p align="center">
<img src="assets/image/Categorized%20Robot%20Task%20Trajectory%20Distribution.png" alt="Dexora Task Categories" width="120%">
</p>
<p align="center">
<img src="assets/image/Robot%20Arm%20Task%20Trajectory%20Distribution.png" alt="Dexora Robot Arm Trajectory Distribution" width="120%">
</p>
<p align="center">
<i>Fig 2. <b>Task Categories & Action Distribution.</b> Unlike standard gripper datasets, Dexora emphasizes high-DoF dexterity. The real-world data distribution includes <b>Dexterous Manipulation (20%)</b> (e.g., <i>Twist Cap</i>, <i>Use Pen</i>, <i>Cut Leek</i>) and <b>Assembly (15%)</b> (e.g., <i>Separate Nested Bowls</i>, <i>Stack Ring Blocks</i>), in addition to <b>Articulated Objects (10%)</b> and <b>Pick-and-Place (55%)</b>.</i>
</p>
Both the episodes and annotations follow the **LIBERO-2.1 standard**, including synchronized **RGB observations**, **robot proprioception**, **actions**, and **language instructions**.
### Object Inventory & Reproducibility
Reproducibility is a **core value** of Dexora. To enable other labs and industry teams to **faithfully recreate** our environments, we release a **curated object inventory** that mirrors the physical setup used in our real-world experiments.
- **Scale**: **347 objects** across **17 semantic categories** (e.g., tools, containers, articulated objects, deformables, daily-use items).
- **Coverage**: Objects are chosen to stress **dexterous control**, **bimanual coordination**, and **long-horizon manipulation**.
- **Procurement**: Every item is linked to **Taobao** and/or **Amazon**, so researchers can rebuild the setup with minimal effort.
<p align="center">
<a href="https://docs.google.com/spreadsheets/d/1L2cgqvIukVziXc0OwpqNkb5j8c3bzC_K/edit?usp=sharing">
<b>📑 Access Dexora Real-world Item List (Google Sheet)</b>
</a>
</p>
### Inventory Metadata Schema
The released Google Sheet follows the schema below:
| Column | Description |
| :------------------------- | :-------------------------------------------------------------------------- |
| **Object Name (EN & CN)** | Bilingual identification for global researchers. |
| **Task Type** | One of: `pick-and-place`, `assemble`, `articulation`, `dexterous`. |
| **Purchase Link** | Direct links to **Taobao** & **Amazon** for easy procurement and restock. |
You can **filter by task type**, **category**, or **store** to design controlled benchmarks or new task suites on top of Dexora.
### B. Dexora Simulation Dataset (Large-Scale)
The Dexora simulation dataset contains **100K episodes** generated in **MuJoCo**, using the same **36-DoF dual-arm, dual-hand** embodiment as the real robot. It provides large-scale, embodiment-matched experience focused on core skills such as **pick-and-place**, **assembly**, and **articulation**, which can be used for pre-training basic competence before **fine-tuning on the real-world dataset**.
### Summary Statistics (Sim vs Real)
| **Split** | **Episodes** | **Frames** | **Hours (approx.)** | **Task Types** |
| :--------------- | -----------: | ---------: | -------------------: | :----------------------------------------------------------------------------- |
| **Simulated** | **100K** | **6.5M** | TBD | Pick-and-place, assembly, articulation |
| **Real-World** | **10K** | **3.2M** | **177.5** | Teleoperated bimanual tasks with high-DoF hands, cluttered scenes, fine-grain object interactions |
## 📂 Data Structure
Dexora follows the **LIBERO-2.1** dataset standard. Each episode is stored as a self-contained trajectory with:
- **Observations**: multi-view RGB (and optionally depth), segmentation masks (when available).
- **Robot State**: joint positions/velocities for dual arms and dual hands, gripper/hand states.
- **Actions**: low-level control commands compatible with 36-DoF bimanual control.
- **Language**: High-level task descriptions. We provide **5 diverse natural language instructions** per task, distributed evenly across all trajectories to enhance linguistic diversity.
An example high-level directory layout is:
```text
data
├── real
│ ├── articulation
│ │ └── ...
│ ├── assembly
│ │ └── ...
│ ├── dexterous manipulation
│ │ ├── data
│ │ │ ├── chunk-000
│ │ │ │ ├── episode_000000.parquet
│ │ │ │ ├── episode_000001.parquet
│ │ │ │ ├── episode_000002.parquet
│ │ │ │ ├── ...
│ │ │ ├── chunk-001
│ │ │ │ ├── ...
│ │ │ ├── ...
│ │ ├── meta
│ │ │ ├── episodes.jsonl
│ │ │ ├── episodes_stats.jsonl
│ │ │ ├── info.json
│ │ │ ├── modality.json
│ │ │ ├── stats.json
│ │ │ ├── tasks.jsonl
│ │ ├── videos
│ │ │ ├── chunk-000
│ │ │ │ ├── observation.images.front
│ │ │ │ │ ├── episode_000000.mp4
│ │ │ │ │ ├── episode_000001.mp4
│ │ │ │ │ ├── ...
│ │ │ │ ├── ...
│ │ │ ├── chunk-001
│ │ │ │ ├── ...
│ │ │ ├── ...
│ ├── pick_and_place
│ │ └── ...
│ ├── ...
├── sim
│ ├── ...
```
> **Note**: The exact folder names and file formats may be updated as we finalize the public release, but the overall **episode-centric LIBERO-2.1 structure** will be preserved.
---
### [meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.1",
"robot_type": "airbot_play",
"total_episodes": 11517,
"total_frames": 2919110,
"total_tasks": 201,
"total_videos": 46068,
"total_chunks": 12,
"chunks_size": 1000,
"fps": 20,
"splits": {
"train": "0:2261"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"observation.images.top": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 20,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.wrist_left": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 20,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.wrist_right": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 20,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.front": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 20,
"video.channels": 3,
"has_audio": false
}
},
"observation.state": {
"dtype": "float32",
"shape": [
39
],
"names": [
"left_arm_joint_1",
"left_arm_joint_2",
"left_arm_joint_3",
"left_arm_joint_4",
"left_arm_joint_5",
"left_arm_joint_6",
"right_arm_joint_1",
"right_arm_joint_2",
"right_arm_joint_3",
"right_arm_joint_4",
"right_arm_joint_5",
"right_arm_joint_6",
"left_hand_joint_1",
"left_hand_joint_2",
"left_hand_joint_3",
"left_hand_joint_4",
"left_hand_joint_5",
"left_hand_joint_6",
"left_hand_joint_7",
"left_hand_joint_8",
"left_hand_joint_9",
"left_hand_joint_10",
"left_hand_joint_11",
"left_hand_joint_12",
"right_hand_joint_1",
"right_hand_joint_2",
"right_hand_joint_3",
"right_hand_joint_4",
"right_hand_joint_5",
"right_hand_joint_6",
"right_hand_joint_7",
"right_hand_joint_8",
"right_hand_joint_9",
"right_hand_joint_10",
"right_hand_joint_11",
"right_hand_joint_12",
"head_joint_1",
"head_joint_2",
"spine_joint"
]
},
"action": {
"dtype": "float32",
"shape": [
39
],
"names": [
"left_arm_joint_1",
"left_arm_joint_2",
"left_arm_joint_3",
"left_arm_joint_4",
"left_arm_joint_5",
"left_arm_joint_6",
"right_arm_joint_1",
"right_arm_joint_2",
"right_arm_joint_3",
"right_arm_joint_4",
"right_arm_joint_5",
"right_arm_joint_6",
"left_hand_joint_1",
"left_hand_joint_2",
"left_hand_joint_3",
"left_hand_joint_4",
"left_hand_joint_5",
"left_hand_joint_6",
"left_hand_joint_7",
"left_hand_joint_8",
"left_hand_joint_9",
"left_hand_joint_10",
"left_hand_joint_11",
"left_hand_joint_12",
"right_hand_joint_1",
"right_hand_joint_2",
"right_hand_joint_3",
"right_hand_joint_4",
"right_hand_joint_5",
"right_hand_joint_6",
"right_hand_joint_7",
"right_hand_joint_8",
"right_hand_joint_9",
"right_hand_joint_10",
"right_hand_joint_11",
"right_hand_joint_12",
"head_joint_1",
"head_joint_2",
"spine_joint"
]
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## 📥 Usage
### 1. Environment Setup
We recommend using **conda** to manage dependencies:
```bash
conda create -n dexora python=3.10 -y
conda activate dexora
# Clone this repository
git clone <your-dexora-repo-url>.git
cd Dexora
# Install Python dependencies (example)
pip install -r requirements.txt
```
If you plan to train or fine-tune VLAs, please ensure that **PyTorch**, **CUDA**, and any required simulation backends (e.g., Isaac, Mujoco) are installed according to your hardware.
### 2. Downloading the Dataset
- **Simulated Dexora**: Download links will be provided on the **project page** (see badge above).
- **Real-World Dexora**: High-resolution teleoperation data (RGB, proprio, actions) will be hosted via a public storage service (e.g., academic server / cloud bucket).
Typical usage:
```bash
# Example directory where you store data
export DEXORA_DATA=/path/to/dexora
# (Optional) Symlink data into this repo
ln -s $DEXORA_DATA data
```
### 3. Loading Episodes (Example)
Below is a minimal Python snippet illustrating how to load a Parquet episode from the real-world dataset:
```python
import pandas as pd
from pathlib import Path
# Example: Loading a Parquet episode from the real-world dataset
root = Path("data/real/dexterous_manipulation/data/chunk-000")
episode_path = root / "episode_000000.parquet"
# Load trajectory using pandas
df = pd.read_parquet(episode_path)
# Access data columns (Observation, Action, Proprioception)
# Note: Columns are typically flattened in Parquet format
print("Available keys:", df.columns)
print("Actions shape:", df["action"].shape) # Example access
print("Language Instruction:", df["language_instruction"][0])
```
---
## 📜 Citation
If you find Dexora useful in your research, please consider citing our paper:
```bibtex
@misc{dexora2026,
title = {Dexora: Open-Source VLA for High-DoF Bimanual Dexterity},
author = {Dexora Team},
year = {2026},
archivePrefix = {arXiv},
eprint = {xxxx.xxxxx},
primaryClass = {cs.RO}
}
```
---
For questions, collaborations, or feedback, please feel free to open an issue or contact the maintainers via the project page.
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