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ShareRobot Dataset
ShareRobot, a high-quality heterogeneous dataset that labels multi-dimensional information, including task planning, object affordance, and end-effector trajectory, effectively enhancing various robotic capabilities.
Overview of ShareRobot Dataset
For planning, we have 51,403 episodes and each with 30 frames. In the process of data generation, we design 5 different templates for each of the 10 question types in RoboVQA [1]. In the process of data generation, we randomly select 2 templates of each question type to generate question-answer pairs for every instance. This process transforms 51,403 instances into 1,027,990 question-answer pairs, with annotators monitoring data generation to maintain the dataset’s integrity.
For Affordance, we have 6,522 images and each with affordance areas aligned with an instruction.
For Trajectory, we have 6,870 images and each with at least 3 {x, y} coordinates aligned with an instruction.
Dataset Sources
ShareRobot dataset contains 23 original datasets from Open X-Embodiment dataset [2], 12 embodiments and 107 types of atomic tasks.
Raw Dataset for Planning
| Raw Dataset | Number of Raws |
|---|---|
| nyu_door_opening_surprising_effectiveness | 421 |
| bridge | 15738 |
| dlr_edan_shared_control_converted_externally_to_rlds | 63 |
| utokyo_xarm_pick_and_place_converted_externally_to_rlds | 92 |
| cmu_stretch | 10 |
| asu_table_top_converted_externally_to_rlds | 109 |
| dlr_sara_pour_converted_externally_to_rlds | 51 |
| utokyo_xarm_bimanual_converted_externally_to_rlds | 27 |
| robo_set | 18164 |
| dobbe | 5200 |
| berkeley_autolab_ur5 | 882 |
| qut_dexterous_manpulation | 192 |
| aloha_mobile | 264 |
| dlr_sara_grid_clamp_converted_externally_to_rlds | 40 |
| ucsd_pick_and_place_dataset_converted_externally_to_rlds | 569 |
| ucsd_kitchen_dataset_converted_externally_to_rlds | 39 |
| jaco_play | 956 |
| utokyo_pr2_opening_fridge_converted_externally_to_rlds | 64 |
| conq_hose_manipulation | 56 |
| fmb | 7836 |
| plex_robosuite | 398 |
| utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds | 189 |
| viola | 44 |
Raw Dataset for Affordance
| Raw Dataset | Number of Raws |
|---|---|
| utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds | 24 |
| utokyo_xarm_pick_and_place_converted_externally_to_rlds | 23 |
| ucsd_kitchen_dataset_converted_externally_to_rlds | 10 |
| ucsd_pick_and_place_dataset_converted_externally_to_rlds | 112 |
| nyu_door_opening_surprising_effectiveness | 85 |
| jaco_play | 171 |
| bridge | 2610 |
| utokyo_pr2_opening_fridge_converted_externally_to_rlds | 12 |
| asu_table_top_converted_externally_to_rlds | 24 |
| viola | 1 |
| berkeley_autolab_ur5 | 122 |
| aloha_mobile | 23 |
| conq_hose_manipulation | 1 |
| dobbe | 717 |
| fmb | 561 |
| plex_robosuite | 13 |
| qut_dexterous_manpulation | 16 |
| robo_set | 1979 |
| dlr_edan_shared_control_converted_externally_to_rlds | 18 |
| Summary | 6522 |
Raw Dataset for Trajectory
| Raw Dataset | Number of Raws |
|---|---|
| utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds | 35 |
| utokyo_xarm_pick_and_place_converted_externally_to_rlds | 36 |
| ucsd_kitchen_dataset_converted_externally_to_rlds | 19 |
| dlr_sara_grid_clamp_converted_externally_to_rlds | 1 |
| ucsd_pick_and_place_dataset_converted_externally_to_rlds | 109 |
| nyu_door_opening_surprising_effectiveness | 74 |
| jaco_play | 175 |
| utokyo_xarm_bimanual_converted_externally_to_rlds | 7 |
| bridge | 2986 |
| utokyo_pr2_opening_fridge_converted_externally_to_rlds | 12 |
| asu_table_top_converted_externally_to_rlds | 22 |
| berkeley_autolab_ur5 | 164 |
| dobbe | 759 |
| fmb | 48 |
| qut_dexterous_manpulation | 29 |
| robo_set | 2374 |
| dlr_sara_pour_converted_externally_to_rlds | 3 |
| dlr_edan_shared_control_converted_externally_to_rlds | 17 |
| Summary | 6870 |
Data Format
Planning
{
"id"{
"id": 0,
"task": "Future_Prediction_Task",
"selected_step": 3,
"conversations": [
{
"from": "human",
"value": "<image 0-25> After <move the grasped banana towards the mug>, what's the most probable next event?"
},
{
"from": "gpt",
"value": "<place the banana into the mug>"
}
],
"image": [
"/path/to/image_0-25"
]
}
}
Affordance
{
"id": 2486,
"meta_data": {
"original_dataset": "bridge",
"original_width": 640,
"original_height": 480
},
"instruction": "place the red fork to the left of the left burner",
"affordance": {
"x": 352.87425387858815,
"y": 186.47871614766484,
"width": 19.296008229513156,
"height": 14.472006172134865
}
Visualize Code
import json
import os
import cv2
import numpy as np
img_dir = '/path/to/your/original/images/dir'
affordance_json = '/path/to/your/affordances/json'
output_img_dir = '/path/to/your/visualized/images/dir'
with open(affordance_json, 'r') as f:
data = json.load(f)
for item in data:
filepath = os.path.join(img_dir, item['id'])
image = cv2.imread(filepath)
color = (255, 0, 0)
thickness = 2
x_min,y_min = item['affordance']['x'], item['affordance']['y']
x_max,y_max = item['affordance']['x']+item['affordance']['width'], item['affordance']['y']+item['affordance']['height']
# 定义矩形的四个顶点坐标
pts = np.array([
[x_min, y_min], # 左上角
[x_max, y_min], # 右上角
[x_max, y_max], # 右下角
[x_min, y_max] # 左下角
], dtype=np.float32)
# 绘制矩形框
cv2.polylines(image, [pts.astype(int)], isClosed=True, color=color, thickness=thickness)
# 获取相对路径并拼接目标路径
relative_path = os.path.relpath(filepath, img_dir) # 获取相对于 img_dir 的相对路径
output_img_path = os.path.join(output_img_dir, relative_path) # 拼接目标路径
# 创建目标文件夹
output_directory = os.path.dirname(output_img_path)
if not os.path.exists(output_directory):
os.makedirs(output_directory)
# 打印调试信息
print(f"Input filepath: {filepath}")
print(f"Output image path: {output_img_path}")
print(f"Output directory: {output_directory}")
# 保存图像
cv2.imwrite(output_img_path, image)
Trajectory
{
"id": 456,
"meta_data": {
"original_dataset": "bridge",
"original_width": 640,
"original_height": 480
},
"instruction": "reach for the carrot",
"points": [
[
265.45454545454544,
120.0
],
[
275.1515151515152,
162.42424242424244
],
[
280.0,
213.33333333333331
],
[
280.0,
259.3939393939394
]
]
},
Visualize Code
import json
import os
from PIL import Image, ImageDraw
trajectory_final = '/path/to/your/trajectory_json'
img_dir = '/path/to/your/original/images/dir'
output_img_dir = '/path/to/your/visualzed/images/dir'
with open(trajectory_final, 'r') as f:
data = json.load(f)
for item in data:
filepath = os.path.join(img_dir, item['id'])
points = item['points']
image = Image.open(filepath).convert("RGB") # 确保图像是 RGB 模式
draw = ImageDraw.Draw(image) # 创建绘图对象
# 定义颜色和线宽
color = (255, 0, 0) # 红色 (RGB 格式)
thickness = 2
scaled_points = [
(point[0], point[1])
for point in points
]
# 按照顺序连接相邻的点
for i in range(len(scaled_points) - 1):
draw.line([scaled_points[i], scaled_points[i + 1]], fill=color, width=thickness)
# 获取相对路径并拼接目标路径
relative_path = os.path.relpath(filepath, img_dir)
output_img_path = os.path.join(output_img_dir, relative_path)
# 创建目标文件夹
output_directory = os.path.dirname(output_img_path)
if not os.path.exists(output_directory):
os.makedirs(output_directory)
# 打印调试信息
print(f"Input filepath: {filepath}")
print(f"Output image path: {output_img_path}")
print(f"Output directory: {output_directory}")
# 保存图像
image.save(output_img_path)
Evaluation
Reference
[1] Pierre Sermanet, Tianli Ding, Jeffrey Zhao, Fei Xia, Debidatta Dwibedi, Keerthana Gopalakrishnan, Christine Chan,Gabriel Dulac-Arnold, Sharath Maddineni, Nikhil J Joshi,et al. Robovqa: Multimodal long-horizon reasoning forrobotics. In ICRA, pages 645–652, 2024.
[2] Abby O’Neill, Abdul Rehman, Abhinav Gupta, AbhiramMaddukuri, Abhishek Gupta, Abhishek Padalkar, AbrahamLee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, et al.Open x-embodiment: Robotic learning datasets and rt-xmodels. arXiv preprint arXiv:2310.08864, 2023.
Citation
@article{ji2025robobrain,
title={RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete},
author={Ji, Yuheng and Tan, Huajie and Shi, Jiayu and Hao, Xiaoshuai and Zhang, Yuan and Zhang, Hengyuan and Wang, Pengwei and Zhao, Mengdi and Mu, Yao and An, Pengju and others},
journal={arXiv preprint arXiv:2502.21257},
year={2025}
}