Datasets:

jasonGUself
/

PetalDex

license: apache-2.0
task_categories:
  - robotics
tags:
  - robotics
  - manipulation
  - bimanual
  - dexterous-hand
  - imitation-learning
  - lerobot
size_categories:
  - 100K<n<1M

PetalDex — Bimanual Dexterous "Arrange Flowers" Dataset

Project website: https://petaldex.github.io/

PetalDex is a bimanual dexterous manipulation dataset for the "arrange flowers" task, collected on a wuji_bimanual robot (two 7‑DoF arms + two 20‑DoF dexterous hands). Each frame provides two RGB camera views (head + right wrist), a 54‑D proprioceptive state, and a 54‑D action, recorded at 30 fps.

The dataset is released in two formats (identical content), so you can use whichever fits your pipeline:

Format	Path	Images	Loader
LeRobot v3.0	`lerobot/<subset>/`	AV1 video (`.mp4`)	`lerobot` / parquet
HDF5	`hdf5/<subset>/`	per‑frame JPEG in `.h5`	`h5py` + `cv2`

Subsets

Subset	Episodes	Frames	Description
`robot_auto`	525	956,892	Robot‑collected "arrange flowers" episodes (merged).
`robot_human_co-creation`	200	268,322	Human–robot co‑creation episodes.

Data schema

observation.state — float32[54] = left_arm(7) + right_arm(7) + left_hand(20) + right_hand(20)
action — float32[54] (same layout as state)
observation.images.head — 224×224×3 RGB
observation.images.right_wrist — 224×224×3 RGB
fps — 30 · robot_type — wuji_bimanual · task — "arrange flowers"

Repository layout

PetalDex/
├── lerobot/
│   ├── robot_auto/                     # LeRobot v3.0 dataset (data/ + videos/ + meta/)
│   └── robot_human_co-creation/
└── hdf5/
    ├── robot_auto/                     # episode_000000.h5 ... + dataset_meta.json
    └── robot_human_co-creation/

Note: this repo hosts four sub‑datasets, so LeRobotDataset("jasonGUself/PetalDex") at the root will not work — load a specific subset folder instead (see below).

Usage

LeRobot v3.0

Download a subset and point LeRobotDataset at its local root:

from huggingface_hub import snapshot_download
from lerobot.datasets.lerobot_dataset import LeRobotDataset

local = snapshot_download(
    repo_id="jasonGUself/PetalDex", repo_type="dataset",
    allow_patterns="lerobot/robot_auto/*",
)
ds = LeRobotDataset("jasonGUself/PetalDex", root=f"{local}/lerobot/robot_auto")
print(ds[0].keys())

HDF5

Each episode is one .h5 file. Images are stored as per‑frame JPEG bytes (variable‑length uint8), decode with OpenCV (returns BGR by cv2 convention):

import h5py, cv2, numpy as np

with h5py.File("hdf5/robot_auto/episode_000000.h5", "r") as f:
    T      = int(f.attrs["num_frames"])            # attrs: fps, task, robot_type, ...
    state  = f["observations/state"][:]            # (T, 54) float32
    action = f["action"][:]                        # (T, 54) float32
    head   = cv2.imdecode(f["observations/images/head"][0],       cv2.IMREAD_COLOR)  # (224,224,3)
    wrist  = cv2.imdecode(f["observations/images/right_wrist"][0], cv2.IMREAD_COLOR)

HDF5 layout per file:

attrs: robot_type, fps, task, episode_index, num_frames,
       image_encoding="jpeg", image_shape=[224,224,3],
       state_dim=54, action_dim=54, state_layout
/observations/images/head          vlen uint8 (T,)   # JPEG bytes per frame
/observations/images/right_wrist   vlen uint8 (T,)
/observations/state                float32 (T, 54)
/action                            float32 (T, 54)
/timestamp                         float32 (T,)
/frame_index                       int64   (T,)

Notes

HDF5 images are re‑encoded to JPEG (quality 95) from the source AV1 video — visually lossless but not bit‑identical to the LeRobot video frames.
The two camera streams are packed into different numbers of video files in the LeRobot format; frame↔episode alignment is handled by meta/episodes/*.parquet.