zfff/sroiv2_strawberry_picking_lab_validation

This dataset was created using LeRobot.

SROI v2 — Strawberry Picking (Lab) — Validation Set

Held-out validation set for the SROI v2 strawberry-picking data (project page, 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 · all SROI datasets
• Training counterpart: 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

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:

{
    "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