X-Humanoid/RoboAug-Datasets

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:

1. annotation_images — keyframe images from multiple tasks and camera views, annotated with bounding boxes in the LabelMe format.
2. 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.imdecode returns BGR, so convert with cv2.cvtColor(img, cv2.COLOR_BGR2RGB).
• master/* are the leader (teleoperation) signals; puppet/* are the follower (actually executed) signals — use puppet/* 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_left carries no usable signal — every frame of every episode is the same still image. Use camera_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},
}