Datasets:
Search is not available for this dataset
video video 2.06 13.8 | label class label 3
classes |
|---|---|
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high | |
0observation.images.cam_high |
End of preview. Expand in Data Studio
RoboPro
RoboPro is an 80-task bimanual manipulation dataset collected on a dual-arm Aloha-Agilex platform. It contains 15,999 expert teleoperation episodes (3.73 M frames at 50 Hz) recorded from three RGB cameras and synchronised 14-DoF joint commands. Each task includes a clean variant and ten cluttered variants (d6…d15) with progressively added distractor objects.
The dataset is released in LeRobot v2.1 format.
At a glance
| Robot | Aloha-Agilex (dual-arm, 14-DoF) |
| Episodes | 15,999 |
| Frames | 3,728,445 |
| FPS | 50 Hz |
| Cameras | 3 × RGB 480×640 H.264 (cam_high, cam_left_wrist, cam_right_wrist) |
| Tasks | 80 base × {clean, d6…d15} variants, 1,622 unique language prompts |
| Format | LeRobot v2.1 (Parquet + MP4) |
| License | CC-BY-4.0 |
Layout
lerobot/roboreal_all_80tasks/
├── meta/
│ ├── info.json # schema, fps, episode count
│ ├── tasks.jsonl # 1,622 task language descriptions
│ ├── episodes.jsonl # per-episode metadata
│ └── episodes_stats.jsonl
├── data/
│ └── chunk-{000..015}/
│ └── episode_{000000..015998}.parquet # 50 Hz proprioception
└── videos/
└── chunk-{000..015}/
└── observation.images.{cam_high,cam_left_wrist,cam_right_wrist}/
└── episode_{000000..015998}.mp4 # H.264 yuv420p, GOP=2
Each Parquet row contains:
observation.state—float32[14]joint stateaction—float32[14]target joint commandtimestamp,frame_index,episode_index,index,task_index
The 14-DoF channel order for both observation.state and action is:
left_waist, left_shoulder, left_elbow, left_forearm_roll,
left_wrist_angle, left_wrist_rotate, left_gripper,
right_waist, right_shoulder, right_elbow, right_forearm_roll,
right_wrist_angle, right_wrist_rotate, right_gripper
Loading
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset(
repo_id="Hoshipu/RoboPro",
root="lerobot/roboreal_all_80tasks", # subfolder within the repo
)
Or stream individual files:
from huggingface_hub import hf_hub_download
import pyarrow.parquet as pq
p = hf_hub_download(
"Hoshipu/RoboPro",
"lerobot/roboreal_all_80tasks/data/chunk-000/episode_000000.parquet",
repo_type="dataset",
)
table = pq.read_table(p)
Croissant
A validated Croissant 1.0 metadata file is included at
croissant.json and conforms to the MLCommons Croissant + RAI specification.
Citation
@dataset{roboPro2026,
title = {RoboPro: 80-Task Bimanual Manipulation Demonstrations on Aloha-Agilex},
author = {Li, Zhiyuan},
year = {2026},
url = {https://huggingface.co/datasets/Hoshipu/RoboPro}
}
Limitations & ethical considerations
- All demonstrations were recorded on a single Aloha-Agilex unit in a fixed laboratory environment; lighting, camera intrinsics, and table geometry do not vary across episodes.
- Tasks are short-horizon (<10 s) tabletop manipulation — no long-horizon, navigation, or contact-rich (insertion, peg-in-hole) tasks.
- Demonstrations reflect the kinematic preferences of a small operator pool; they are not guaranteed to be task-optimal.
- No human faces, voices, biometric or other personal information is captured. Scenes consist of inanimate objects on a laboratory table.
See croissant.json for the full RAI block.
- Downloads last month
- 74