--- 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 [![Visualize this dataset](https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg)](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} } ```