--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). # SROI v2 — Strawberry Picking (Lab) — Validation Set ![Example frames from four episodes](examples/montage.jpg) Held-out **validation set** for the SROI v2 strawberry-picking data ([project page](https://agroboticsresearch.github.io/sroi_v2/), Zhejiang University): 100 human strawberry-picking demonstrations recorded with the **SROI V2 handheld data-acquisition device** — a UMI-style gripper with an integrated Intel RealSense D405 stereo camera — on live plants in a laboratory setup. **No robot arm is involved during collection**: the 7-DoF end-effector actions are recovered off-line (ORB-SLAM3 stereo SLAM with gripper mask for the device trajectory; AprilTags for the gripper opening), and the learned policy is deployed on a robot arm carrying the same end effector, whose camera viewpoint is identical to the one in these recordings. - **Task:** `pick the strawberry` (single task, all 100 episodes) - **Collection:** human demonstrations with the SROI V2 handheld device (UMI-style — not recorded on a robot arm) - **Camera:** Intel RealSense D405 mounted on the device (18 mm stereo baseline, zero distortion), 480×640 RGB, 30 fps, first-person view - **Project:** [agroboticsresearch.github.io/sroi_v2](https://agroboticsresearch.github.io/sroi_v2/) · [all SROI datasets](https://agroboticsresearch.github.io/sroi_datasets/) - **Training counterpart:** [`zfff/sroiv2_strawberry_picking_lab_1459_occlusion`](https://huggingface.co/datasets/zfff/sroiv2_strawberry_picking_lab_1459_occlusion) This collection was **recorded on a different day (2026-07-14)** than the training recordings (2026-07-09 onward), so there is **no episode leakage** between train and validation. Task, fps, and schema are identical, so it drops in as a validation holdout. ## Quickstart ```python from lerobot.datasets.lerobot_dataset import LeRobotDataset dataset = LeRobotDataset("zfff/sroiv2_strawberry_picking_lab_validation") episode = dataset[0] # dict with "observation.images.camera", "action", ... ``` ## Dataset Summary | | | |---|---| | Episodes | 100 | | Frames | 9,274 | | fps | 30 | | Format | LeRobot v3.0 (Parquet + AV1 video) | | Observation | `observation.images.camera` — 480×640×3 RGB video | | Action | `action` — 7-D float32: `ee.x, ee.y, ee.z, ee.wx, ee.wy, ee.wz, ee.gripper_pos` | | Total size | ~155 MB | ## Processing Recorded MP4s → frame decode → ORB-SLAM3 stereo trajectory estimation (**with gripper mask**) → trajectory transform → AprilTag-based gripper pose estimation (median filter 3) → visual QC → LeRobot conversion. - All 100 recorded episodes passed visual QC (`ok` rating, 100 kept / 0 dropped). - The masked-SLAM processing matches the training pipeline, so train and validation trajectories are directly comparable. - The gripper position channel is normalized to `[0, 1]` with one robust pooled range across these 100 episodes. - Per-episode camera intrinsics are preserved under `meta/camera_info/`. ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v3.0", "fps": 30, "features": { "observation.images.camera": { "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": 30, "video.channels": 3, "has_audio": false } }, "action": { "dtype": "float32", "names": ["ee.x", "ee.y", "ee.z", "ee.wx", "ee.wy", "ee.wz", "ee.gripper_pos"], "shape": [7] }, "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": 100, "total_frames": 9274, "total_tasks": 1, "chunks_size": 1000, "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": "so100", "splits": {"train": "0:100"} } ``` > **Note:** `robot_type: "so100"` in `meta/info.json` is a hardcoded default of the conversion script (`sroi_to_lerobot.py`) and does **not** describe the collection rig — this data is human-collected with the SROI V2 handheld device. ## Citation If you use this dataset, you are welcome to cite: > Hou, L., Lu, W., Wang, Y., Peng, C., & Fei, Z. (2025). Strawberry Robotic Operation Interface: An Open-Source Device for Collecting Dexterous Manipulation Data in Robotic Strawberry Cultivation. *IFAC-PapersOnLine, 59*(23), 303–308. ## License Apache-2.0