metadata
license: apache-2.0
pretty_name: RoboAug Datasets
task_categories:
- robotics
tags:
- robotics
- manipulation
- teleoperation
- data-augmentation
- hdf5
size_categories:
- 1K<n<10K
RoboAug Datasets
These datasets accompany the paper RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation (arXiv:2602.14032, project page, code).
This repository contains two kinds of data:
annotation_images— keyframe images from multiple tasks and camera views, annotated with bounding boxes in the LabelMe format.- Real-robot manipulation datasets — teleoperated demonstrations stored as per-episode HDF5 files (multi-camera RGB/depth images + robot proprioceptive states) on three platforms:
| Dataset | Platform | Tasks | Episodes | Frames | Cameras | Language | Size |
|---|---|---|---|---|---|---|---|
Single_Arm_UR_5e |
Universal Robots UR5e, single 6-DoF arm + gripper | 5 | 789 | 105,704 | up to 6: front / left / right / top / wrist_left / wrist_right | yes | 149 GB |
AgileX_Cobot_Magic_V2.0 |
AgileX Cobot Magic V2.0, dual 7-DoF arms | 5 | 254 | 97,359 | camera_top | yes | 8.6 GB |
Tien_Kung_2.0 |
Tien Kung 2.0 humanoid, dual 7-DoF arms + grippers | 5 | 258 | 45,670 | camera_head | no | 33 GB |
Download
pip install -U "huggingface_hub[hf_transfer]"
# Everything
hf download X-Humanoid/RoboAug-Datasets --repo-type dataset --local-dir ./data
# A single subset, e.g. the UR5e demonstrations
hf download X-Humanoid/RoboAug-Datasets --repo-type dataset \
--include "Single_Arm_UR_5e/*" --local-dir ./data
Layout of the robot datasets
<dataset>/
└── <task_name>/
└── success_episodes/
└── <episode_id>/ # e.g. 0307_154247
└── data/trajectory.hdf5
There is no train/val split: every episode of a task sits directly under
success_episodes/ and appears exactly once. Build your own split if needed.
Common conventions
- Images are stored as encoded byte strings (HDF5 vlen
uint8), one per time step. Color frames are JPEG (decode to(H, W, 3)uint8); depth frames are PNG (decode to(H, W)uint16, millimeters). - JPEGs use the standard color order: they display correctly in any image viewer
and PIL decodes them to RGB.
cv2.imdecodereturns BGR, so convert withcv2.cvtColor(img, cv2.COLOR_BGR2RGB). master/*are the leader (teleoperation) signals;puppet/*are the follower (actually executed) signals — usepuppet/*as action / state targets.- Camera resolutions differ across cameras and datasets; read the shape from the decoded array.
1. Single_Arm_UR_5e
| Key | Shape | dtype | Description |
|---|---|---|---|
language_instruction |
scalar | string | natural-language task instruction |
is_intervene |
(T,) |
int | human-intervention flag (1 = teleoperated frame) |
master/arm_joint_position |
(T, 6) |
float64 | leader 6-axis joint angles |
master/hand_joint_position |
(T, 1) |
float64 | leader gripper |
puppet/arm_joint_position |
(T, 6) |
float32 | follower 6-axis joint angles |
puppet/hand_joint_position |
(T, 1) |
float32 | follower gripper |
puppet/end_effector |
(T, 6) or (T, 7) |
float32 | follower TCP pose (6 = position + Euler, 7 = position + quaternion) |
observations/rgb_images/<camera> |
(T,) |
object | JPEG color: camera_front / camera_top 720x1280, other cameras 480x640 |
observations/depth_images/<camera> |
(T,) |
object | PNG uint16 depth, same resolution as the color stream |
| Task | Episodes | Frames | Cameras |
|---|---|---|---|
| ur_move_lemon_from_plate_to_bowl | 295 | 46,766 | front, left, top, wrist_left |
| ur_put_corn_into_the_pot | 294 | 33,725 | front, left, top, wrist_left |
| ur_stack_bowl_250523 | 100 | 11,609 | front, left, right, top, wrist_left, wrist_right |
| ur_open_drawer_and_put_corn | 50 | 7,289 | front, left, right, top, wrist_left, wrist_right |
| ur_put_carrot_and_close_drawer | 50 | 6,315 | front, left, right, top, wrist_left, wrist_right |
Known issue: in
ur_put_carrot_and_close_drawer,camera_wrist_leftcarries no usable signal — every frame of every episode is the same still image. Usecamera_wrist_right(or the other views) for this task.
2. AgileX_Cobot_Magic_V2.0
Two arms, with each arm's gripper packed into the joint vector.
| Key | Shape | dtype | Description |
|---|---|---|---|
language_instruction |
scalar | string | natural-language task instruction |
is_intervene |
(T,) |
int | human-intervention flag |
master/arm_joint_position |
(T, 14) |
float64 | leader, both arms: 2 x (6 joints + 1 gripper) |
puppet/arm_joint_position |
(T, 14) |
float32 | follower, both arms (same layout) |
puppet/end_effector |
(T, 12) |
float32 | follower TCP pose, both arms: 2 x (position + Euler) |
observations/rgb_images/camera_top |
(T,) |
object | JPEG color, 480x640 |
observations/depth_images/camera_top |
(T,) |
object | PNG uint16 depth, 400x640 |
| Task | Episodes | Frames |
|---|---|---|
| agilex_1_open_pot_and_put_corn | 50 | 22,276 |
| agilex_1_pick_banana_and_close_drawer | 49 | 17,311 |
| agilex_1_put_corn_into_plate | 55 | 18,497 |
| agilex_1_stack_bowl_250524 | 51 | 18,669 |
| agilex_1_upright_mug | 49 | 20,606 |
3. Tien_Kung_2.0
Camera data lives under camera_observations (color_images instead of
rgb_images), left/right arms and grippers are separate groups each holding a
data dataset, and there is no language_instruction or end-effector pose.
| Key | Shape | dtype | Description |
|---|---|---|---|
camera_observations/color_images/camera_head |
(T,) |
object | JPEG color, 720x1280 |
camera_observations/depth_images/camera_head |
(T,) |
object | PNG uint16 depth, 720x1280 |
camera_observations/is_intervene |
(T,) |
bool | human-intervention flag |
camera_observations/timestamp |
(T,) |
float64 | Unix timestamp per frame |
master/arm_{left,right}_position_align/data |
(T, 7) |
float64 | leader arm 7-DoF joints |
master/end_effector_{left,right}_position_align/data |
(T, 1) |
float64 | leader gripper |
puppet/arm_{left,right}_position_align/data |
(T, 7) |
float32 | follower arm 7-DoF joints |
puppet/end_effector_{left,right}_position_align/data |
(T, 1) |
float32 | follower gripper |
| Task | Episodes | Frames |
|---|---|---|
| tienkung_16_collect_balls_251016 | 51 | 6,840 |
| tienkung_16_lay_bowl_plate_251031 | 55 | 9,570 |
| tienkung_16_oven | 50 | 8,982 |
| tienkung_16_select_yellow_button | 52 | 9,676 |
| tienkung_16_weight_apple | 50 | 10,602 |
Reading an episode
import cv2
import h5py
import numpy as np
def decode_rgb(buf):
return cv2.cvtColor(cv2.imdecode(np.asarray(buf), cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB)
with h5py.File("Single_Arm_UR_5e/ur_put_corn_into_the_pot/success_episodes/0307_154247/data/trajectory.hdf5", "r") as f:
instr = f["language_instruction"][()].decode("utf-8")
rgb = decode_rgb(f["observations/rgb_images/camera_front"][0]) # (720, 1280, 3) RGB
depth = cv2.imdecode(np.asarray(f["observations/depth_images/camera_front"][0]), cv2.IMREAD_UNCHANGED)
arm = f["puppet/arm_joint_position"][:] # (T, 6)
hand = f["puppet/hand_joint_position"][:] # (T, 1)
The RoboAug repository also ships a
loader for Single_Arm_UR_5e (ReadH5Files in src/read_h5.py); pass
"to_rgb": True in its robot_infor dict to get RGB arrays.
License and citation
Released under the Apache License 2.0.
@misc{wang2026roboaugannotationhundredsscenes,
title={RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation},
author={Xinhua Wang and Kun Wu and Zhen Zhao and Hu Cao and Yinuo Zhao and Zhiyuan Xu and Meng Li and Shichao Fan and Di Wu and Yixue Zhang and Ning Liu and Zhengping Che and Jian Tang},
year={2026},
eprint={2602.14032},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2602.14032},
}