| --- |
| license: mit |
| --- |
| <p align="center"> |
| <h2>Dexora: Open-Source VLA for High-DoF Bimanual Dexterity</h2> |
| </p> |
|
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| <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> |
|
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|
| ## 🔥 News & Updates |
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|
| - **2025-12-03**: Released the full **Real-World Dataset** (**12.2K episodes**) on [Hugging Face](https://huggingface.co/datasets/Dexora/Dexora_Real-World_Dataset). |
| - **2025-12-12**: Released the **task-level** view of the **Real-World Dataset** (one folder per high-level task) on [Hugging Face](https://huggingface.co/datasets/Dexora/Dexora_Real-World_Dataset). |
| - **Coming soon**: We will open-source the full **100K-episode simulation dataset**. |
|
|
| -- |
|
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| ## 📊 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. |
|
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| ### A. Dexora Real-World Dataset (High-Fidelity) |
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| The Dexora real-world dataset consists of **12.2K 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. |
|
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| <p align="center"> |
| <img src="assets/image/dataset.gif" alt="Dexora Multi-view Dataset" width="100%"> |
| </p> |
|
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| <p> |
| <i>Video 1. <b>Synchronized Multi-View Recordings.</b> High-resolution streams from four synchronized views — an ego-centric head-mounted camera, left and right wrist-mounted cameras, and a static third-person scene camera — synchronized with 36-DoF robot proprioception.</i> |
| </p> |
|
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| <p align="center"> |
| <img src="assets/image/real-data.JPG" alt="Dexora Real-World Dataset Mosaic" width="100%"> |
| </p> |
|
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| <p> |
| <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_Robot_Task_Trajectory_Distribution.png" alt="Dexora Task Categories" width="120%"> |
| </p> |
|
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| <p align="center"> |
| <img src="assets/image/Robot_Arm_Task_Trajectory_Distribution.png" alt="Dexora Robot Arm Trajectory Distribution" width="120%"> |
| </p> |
|
|
| <p> |
| <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. |
|
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| - **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. | |
|
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| You can **filter by task type**, **category**, or **store** to design controlled benchmarks or new task suites on top of Dexora. |
|
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|
|
| ### B. Dexora Simulation Dataset (Large-Scale) |
|
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| 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** | **——** | **——** | TBD | Pick-and-place, assembly, articulation | |
| | **Real-World** | **12.2K** | **2.92M** | **40.5** | 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: |
|
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| - **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. |
|
|
| We provide an additional **task-level** view (one folder per high-level task) on [Hugging Face](https://huggingface.co/datasets/Dexora/Dexora_Real-World_Dataset), alongside the original episode-centric **LIBERO-2.1** layout. The latest complete structure is: |
|
|
| ```text |
| Dexora_Real-World_Dataset |
| ├── airbot_articulation |
| │ ├── data |
| │ │ ├── chunk-000 |
| │ │ │ ├── episode_000000.parquet |
| │ │ │ ├── episode_000001.parquet |
| │ │ │ ├── ... |
| │ │ ├── chunk-001 |
| │ │ ├── ... |
| │ ├── videos |
| │ │ ├── chunk-000 |
| │ │ │ ├── observation.images.front |
| │ │ │ │ ├── episode_000000.mp4 |
| │ │ │ │ ├── episode_000001.mp4 |
| │ │ │ │ ├── ... |
| │ │ ├── chunk-001 |
| │ │ ├── ... |
| │ ├── meta |
| │ │ ├── info.json |
| │ │ ├── episodes.jsonl |
| │ │ ├── episodes_stats.jsonl |
| │ │ ├── modality.json |
| │ │ ├── stats.json |
| │ │ ├── tasks.jsonl |
| ├── airbot_assemble |
| │ └── ... |
| ├── airbot_dexterous |
| │ └── ... |
| ├── airbot_pick_and_place |
| │ └── ... |
| ├── task_level_episodes |
| │ ├── apply_tape_to_bottle |
| │ ├── arrange_apple_peach_pear |
| │ ├── fold_towel_bimanual |
| │ ├── move_toy_cars_from_plate_to_table |
| │ ├── ... |
| └── README.md |
| ``` |
| ``` |
| |
| > **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. You can also reach us directly at pjr24@mails.tsinghua.edu.cn |
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