| | --- |
| | 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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