| --- |
| language: |
| - en |
| license: mit |
| tags: |
| - robotics |
| - lerobot |
| - robot-manipulation |
| - rgb |
| - depth |
| - point-cloud |
| - language |
| pretty_name: droid_3d |
| size_categories: |
| - n>1M |
| task_categories: |
| - robotics |
| - video-classification |
| task_ids: |
| - grasping |
| - task-planning |
| --- |
| |
| This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). |
|
|
| > **IMPORTANT:** Standard LeRobot (huggingface/lerobot) does **not** currently support depth videos or point clouds. To load this dataset, you **must** use the modified codebase: |
| > **[ZibinDong/lerobotdataset3d](https://github.com/ZibinDong/lerobotdataset3d)**, which adds full decoding support for depth videos (H.265/H.264/FFV1) and quantized point clouds. |
|
|
| ## Dataset Description |
|
|
| **droid_3d** is a large-scale robot manipulation dataset collected with the DROID data collection platform. It contains multi-view RGB videos, depth videos, point clouds, robot actions, and natural language task descriptions. The dataset is designed for training vision-language-action models and 3D-aware robot policies. |
| |
| - **Homepage:** https://github.com/ZibinDong/lerobotdataset3d |
| - **Paper:** [EmbodiedMAE: A Unified 3D Multi-Modal Representation for Robot Manipulation](https://arxiv.org/abs/2505.10105) |
| - **License:** MIT |
| |
| ### Dataset Summary |
| |
| The dataset comprises **58,201 episodes** with a total of **18,083,626 frames**, recorded at **15 FPS** using a multi-camera setup (wrist + 2 external cameras). Each frame captures: |
| |
| - **RGB videos** from 3 camera views (wrist, external_0, external_1) at 224×398 resolution, encoded with AV1. |
| - **Depth videos** from the same 3 views at 224×398 resolution, encoded with H.265 (h265_uint12), with a depth range of 2000 mm. |
| - **Point clouds** (max 2048 points) for each camera, with quantized XYZ coordinates in the ranges: x∈[−1.0, 1.0], y∈[−1.0, 1.0], z∈[0.0, 1.6]. |
| - **8-dimensional action vectors** (float32). |
| - **Up to 3 natural language annotations** per episode describing the task. |
|
|
| The dataset covers **23,858 distinct tasks** and is split into training data only (no evaluation split). Total dataset size is approximately **1.3 TB**. |
|
|
| ## Dataset Structure |
|
|
| [meta/info.json](https://huggingface.co/datasets/ZibinDong/droid_3d/resolve/main/meta/info.json): |
|
|
| ```json |
| { |
| "codebase_version": "v3.0", |
| "fps": 15, |
| "features": { |
| "observation.images.wrist": { |
| "dtype": "video", |
| "shape": [224, 398, 3], |
| "info": { |
| "video.height": 224, |
| "video.width": 398, |
| "video.codec": "av1", |
| "video.pix_fmt": "yuv420p", |
| "video.is_depth_map": false, |
| "video.fps": 15, |
| "video.channels": 3, |
| "has_audio": false |
| } |
| }, |
| "observation.images.external_0": { |
| "dtype": "video", |
| "shape": [224, 398, 3], |
| "info": { |
| "video.height": 224, |
| "video.width": 398, |
| "video.codec": "av1", |
| "video.pix_fmt": "yuv420p", |
| "video.is_depth_map": false, |
| "video.fps": 15, |
| "video.channels": 3, |
| "has_audio": false |
| } |
| }, |
| "observation.images.external_1": { |
| "dtype": "video", |
| "shape": [224, 398, 3], |
| "info": { |
| "video.height": 224, |
| "video.width": 398, |
| "video.codec": "av1", |
| "video.pix_fmt": "yuv420p", |
| "video.is_depth_map": false, |
| "video.fps": 15, |
| "video.channels": 3, |
| "has_audio": false |
| } |
| }, |
| "observation.depth.wrist": { |
| "dtype": "depth_video", |
| "shape": [224, 398, 1], |
| "scale": "uint12_mm", |
| "encoding": "h265_uint12", |
| "depth_range_mm": 2000.0 |
| }, |
| "observation.depth.external_0": { |
| "dtype": "depth_video", |
| "shape": [224, 398, 1], |
| "scale": "uint12_mm", |
| "encoding": "h265_uint12", |
| "depth_range_mm": 2000.0 |
| }, |
| "observation.depth.external_1": { |
| "dtype": "depth_video", |
| "shape": [224, 398, 1], |
| "scale": "uint12_mm", |
| "encoding": "h265_uint12", |
| "depth_range_mm": 2000.0 |
| }, |
| "observation.pointcloud.wrist": { |
| "dtype": "pointcloud", |
| "shape": [null, 3], |
| "max_points": 2048, |
| "features": [], |
| "quantize_xyz": true, |
| "xyz_range_x": [-1.0, 1.0], |
| "xyz_range_y": [-1.0, 1.0], |
| "xyz_range_z": [0.0, 1.6] |
| }, |
| "observation.pointcloud.external_0": { |
| "dtype": "pointcloud", |
| "shape": [null, 3], |
| "max_points": 2048, |
| "features": [], |
| "quantize_xyz": true, |
| "xyz_range_x": [-1.0, 1.0], |
| "xyz_range_y": [-1.0, 1.0], |
| "xyz_range_z": [0.0, 1.6] |
| }, |
| "observation.pointcloud.external_1": { |
| "dtype": "pointcloud", |
| "shape": [null, 3], |
| "max_points": 2048, |
| "features": [], |
| "quantize_xyz": true, |
| "xyz_range_x": [-1.0, 1.0], |
| "xyz_range_y": [-1.0, 1.0], |
| "xyz_range_z": [0.0, 1.6] |
| }, |
| "action": { |
| "dtype": "float32", |
| "shape": [8] |
| }, |
| "language_1": { |
| "dtype": "string", |
| "shape": [1] |
| }, |
| "language_2": { |
| "dtype": "string", |
| "shape": [1] |
| }, |
| "language_3": { |
| "dtype": "string", |
| "shape": [1] |
| }, |
| "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 |
| } |
| }, |
| "total_episodes": 58201, |
| "total_frames": 18083626, |
| "total_tasks": 23858, |
| "chunks_size": 1000, |
| "data_files_size_in_mb": 100, |
| "video_files_size_in_mb": 200, |
| "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet", |
| "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4", |
| "robot_type": null, |
| "splits": { |
| "train": "0:58201" |
| } |
| } |
| ``` |
|
|
| ### Data Format |
|
|
| The dataset follows the [LeRobot format (v3.0)](https://github.com/huggingface/lerobot) with the following directory layout: |
|
|
| ``` |
| droid_3d/ |
| ├── data/ |
| │ └── chunk-000/ |
| │ └── file-{000-999}.parquet # Action and metadata (636 MB) |
| ├── videos/ |
| │ ├── observation.images.wrist/ |
| │ │ └── chunk-000/ # Wrist RGB videos |
| │ ├── observation.images.external_0/ |
| │ │ └── chunk-000/ # External camera 0 RGB videos |
| │ ├── observation.images.external_1/ |
| │ │ └── chunk-000/ # External camera 1 RGB videos |
| │ ├── observation.depth.wrist/ |
| │ │ └── chunk-000/ # Wrist depth videos (H.265) |
| │ ├── observation.depth.external_0/ |
| │ │ └── chunk-000/ # External camera 0 depth videos (H.265) |
| │ └── observation.depth.external_1/ |
| │ └── chunk-000/ # External camera 1 depth videos (H.265) |
| ├── pointclouds/ |
| │ ├── observation.pointcloud.wrist/ |
| │ │ └── chunk-000/ # Wrist point clouds (Parquet) |
| │ ├── observation.pointcloud.external_0/ |
| │ │ └── chunk-000/ # External camera 0 point clouds (Parquet) |
| │ └── observation.pointcloud.external_1/ |
| │ └── chunk-000/ # External camera 1 point clouds (Parquet) |
| ├── meta/ |
| │ ├── info.json # Dataset metadata |
| │ ├── stats.json # Dataset statistics |
| │ ├── tasks.parquet # Task definitions |
| │ └── episodes/ # Episode metadata |
| └── README.md |
| ``` |
|
|
| ### Features |
|
|
| | Feature | Dtype | Shape | Details | |
| |---------|-------|-------|---------| |
| | `observation.images.wrist` | video | (224, 398, 3) | AV1, 15 FPS, yuv420p | |
| | `observation.images.external_0` | video | (224, 398, 3) | AV1, 15 FPS, yuv420p | |
| | `observation.images.external_1` | video | (224, 398, 3) | AV1, 15 FPS, yuv420p | |
| | `observation.depth.wrist` | depth_video | (224, 398, 1) | H.265 uint12, scale=uint12_mm, range=2000 mm | |
| | `observation.depth.external_0` | depth_video | (224, 398, 1) | H.265 uint12, scale=uint12_mm, range=2000 mm | |
| | `observation.depth.external_1` | depth_video | (224, 398, 1) | H.265 uint12, scale=uint12_mm, range=2000 mm | |
| | `observation.pointcloud.wrist` | pointcloud | (2048, 3) | Quantized XYZ, x∈[−1,1], y∈[−1,1], z∈[0,1.6] | |
| | `observation.pointcloud.external_0` | pointcloud | (2048, 3) | Quantized XYZ, x∈[−1,1], y∈[−1,1], z∈[0,1.6] | |
| | `observation.pointcloud.external_1` | pointcloud | (2048, 3) | Quantized XYZ, x∈[−1,1], y∈[−1,1], z∈[0,1.6] | |
| | `action` | float32 | (8,) | Robot action vector | |
| | `language_1` | string | (1,) | First language annotation | |
| | `language_2` | string | (1,) | Second language annotation | |
| | `language_3` | string | (1,) | Third language annotation | |
| | `timestamp` | float32 | (1,) | Frame timestamp | |
| | `frame_index` | int64 | (1,) | Frame index within episode | |
| | `episode_index` | int64 | (1,) | Episode identifier | |
| | `index` | int64 | (1,) | Global frame index | |
| | `task_index` | int64 | (1,) | Task identifier | |
|
|
| ### Splits |
|
|
| | Split | Episodes | Frames | |
| |-------|----------|--------| |
| | train | 58,201 | 18,083,626 | |
|
|
| ### Dataset Size |
|
|
| - Videos: 823 GB |
| - Point clouds: 461 GB |
| - Parquet data: 636 MB |
| - **Total: ~1.3 TB** |
|
|
| ## Usage |
|
|
| > ⚠️ Because standard LeRobot lacks depth video and point cloud decoding, you must install and use **[lerobotdataset3d](https://github.com/ZibinDong/lerobotdataset3d)** to load this dataset. |
|
|
| ### Install |
|
|
| ```bash |
| pip install git+https://github.com/ZibinDong/lerobotdataset3d.git |
| ``` |
|
|
| ### Load the dataset |
|
|
| ```python |
| from lerobotdataset3d import LeRobotDatasetDepthPointcloud |
| |
| dataset = LeRobotDatasetDepthPointcloud( |
| repo_id="ZibinDong/droid_3d", |
| root="/local_path/to/droid_3d", |
| ) |
| |
| item = dataset[0] |
| |
| # RGB video frames: (3, H, W) float32 in [0, 255] |
| item["observation.images.wrist"].shape # torch.Size([3, 224, 398]) |
| item["observation.images.external_0"] |
| |
| # Depth frames: (1, H, W) float32 in meters |
| item["observation.depth.wrist"].shape # torch.Size([1, 224, 398]) |
| item["observation.depth.external_0"] |
| |
| # Point clouds: (max_points, 3) float32 in meters |
| item["observation.pointcloud.wrist"].shape # torch.Size([2048, 3]) |
| item["observation.pointcloud.external_0"] |
| |
| # Actions, language, and metadata |
| item["action"] # torch.Size([8]) |
| item["language_1"] # str |
| item["language_2"] |
| item["language_3"] |
| item["episode_index"] |
| item["frame_index"] |
| item["index"] |
| item["timestamp"] |
| item["task_index"] |
| ``` |
|
|
| ### Advanced: temporal window sampling |
|
|
| ```python |
| from lerobotdataset3d import LeRobotDatasetDepthPointcloud |
| |
| dataset = LeRobotDatasetDepthPointcloud( |
| repo_id="ZibinDong/droid_3d", |
| root="/local_path/to/droid_3d", |
| delta_timestamps={ |
| "observation.images.wrist": [-0.1, 0.0, 0.1], |
| "action": [-0.1, 0.0, 0.1, 0.2, 0.3], |
| }, |
| ) |
| ``` |
|
|
| ### Visualize online |
|
|
| [](https://huggingface.co/spaces/lerobot/visualize_dataset?path=ZibinDong/droid_3d) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{dong2025embodiedmae, |
| title = {EmbodiedMAE: A Unified 3D Multi-Modal Representation for Robot Manipulation}, |
| author = {Dong, Zibin and Ni, Fei and Yuan, Yifu and Li, Yinchuan and Hao, Jianye}, |
| journal = {arXiv preprint arXiv:2505.10105}, |
| year = {2025} |
| } |
| ``` |
|
|
