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Error code: DatasetGenerationError Exception: ArrowCapacityError Message: array cannot contain more than 2147483646 bytes, have 5368709120 Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1626, in _prepare_split_single writer.write(example, key) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 538, in write self.write_examples_on_file() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 496, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 610, in write_batch self.write_table(pa_table, writer_batch_size) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 625, in write_table pa_table = embed_table_storage(pa_table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2271, in embed_table_storage arrays = [ File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in <listcomp> embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name] File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2141, in embed_array_storage return feature.embed_storage(array) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 271, in embed_storage bytes_array = pa.array( File "pyarrow/array.pxi", line 343, in pyarrow.lib.array File "pyarrow/array.pxi", line 42, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 5368709120 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1635, in _prepare_split_single num_examples, num_bytes = writer.finalize() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 637, in finalize self.write_examples_on_file() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 496, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 610, in write_batch self.write_table(pa_table, writer_batch_size) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 625, in write_table pa_table = embed_table_storage(pa_table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2271, in embed_table_storage arrays = [ File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in <listcomp> embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name] File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2141, in embed_array_storage return feature.embed_storage(array) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 271, in embed_storage bytes_array = pa.array( File "pyarrow/array.pxi", line 343, in pyarrow.lib.array File "pyarrow/array.pxi", line 42, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 5368709120 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1433, in compute_config_parquet_and_info_response parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet( File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 989, in stream_convert_to_parquet builder._prepare_split( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1487, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
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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}
}
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