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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 7 new columns ({'avg_force_x', 'avg_force_z', 'avg_force_y', 'contact_type', 'object_material', 'n_measurements', 'max_force'}) and 9 missing columns ({'init_x', 'init_z', 'path_length', 'final_x', 'n_steps', 'init_y', 'object', 'final_y', 'final_z'}).
This happened while the csv dataset builder was generating data using
hf://datasets/shangshang/embodied-ai-dataset/force_tactile.csv (at revision 82ee681daaccf944d9e349bbfb72da02cfcf58d2), ['hf://datasets/shangshang/embodied-ai-dataset@82ee681daaccf944d9e349bbfb72da02cfcf58d2/end_effector_poses.csv', 'hf://datasets/shangshang/embodied-ai-dataset@82ee681daaccf944d9e349bbfb72da02cfcf58d2/force_tactile.csv', 'hf://datasets/shangshang/embodied-ai-dataset@82ee681daaccf944d9e349bbfb72da02cfcf58d2/joint_configurations.csv', 'hf://datasets/shangshang/embodied-ai-dataset@82ee681daaccf944d9e349bbfb72da02cfcf58d2/language_instructions.csv', 'hf://datasets/shangshang/embodied-ai-dataset@82ee681daaccf944d9e349bbfb72da02cfcf58d2/robot_trajectory.csv', 'hf://datasets/shangshang/embodied-ai-dataset@82ee681daaccf944d9e349bbfb72da02cfcf58d2/vla_combined_dataset.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
sample_id: int64
task: string
object_material: string
n_measurements: int64
avg_force_x: double
avg_force_y: double
avg_force_z: double
max_force: double
contact_type: string
success: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1480
to
{'sample_id': Value('int64'), 'task': Value('string'), 'object': Value('string'), 'n_steps': Value('int64'), 'init_x': Value('float64'), 'init_y': Value('float64'), 'init_z': Value('float64'), 'final_x': Value('float64'), 'final_y': Value('float64'), 'final_z': Value('float64'), 'path_length': Value('float64'), 'success': Value('int64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 7 new columns ({'avg_force_x', 'avg_force_z', 'avg_force_y', 'contact_type', 'object_material', 'n_measurements', 'max_force'}) and 9 missing columns ({'init_x', 'init_z', 'path_length', 'final_x', 'n_steps', 'init_y', 'object', 'final_y', 'final_z'}).
This happened while the csv dataset builder was generating data using
hf://datasets/shangshang/embodied-ai-dataset/force_tactile.csv (at revision 82ee681daaccf944d9e349bbfb72da02cfcf58d2), ['hf://datasets/shangshang/embodied-ai-dataset@82ee681daaccf944d9e349bbfb72da02cfcf58d2/end_effector_poses.csv', 'hf://datasets/shangshang/embodied-ai-dataset@82ee681daaccf944d9e349bbfb72da02cfcf58d2/force_tactile.csv', 'hf://datasets/shangshang/embodied-ai-dataset@82ee681daaccf944d9e349bbfb72da02cfcf58d2/joint_configurations.csv', 'hf://datasets/shangshang/embodied-ai-dataset@82ee681daaccf944d9e349bbfb72da02cfcf58d2/language_instructions.csv', 'hf://datasets/shangshang/embodied-ai-dataset@82ee681daaccf944d9e349bbfb72da02cfcf58d2/robot_trajectory.csv', 'hf://datasets/shangshang/embodied-ai-dataset@82ee681daaccf944d9e349bbfb72da02cfcf58d2/vla_combined_dataset.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
sample_id int64 | task string | object string | n_steps int64 | init_x float64 | init_y float64 | init_z float64 | final_x float64 | final_y float64 | final_z float64 | path_length float64 | success int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|
0 | place | tool | 34 | 0.483 | 0.352 | 0.307 | 0.246 | 0.189 | 0.213 | 0.302 | 0 |
1 | insert | bottle | 22 | -0.271 | 0.357 | 0.444 | -0.328 | 0.317 | 0.368 | 0.103 | 1 |
2 | pick | key | 24 | 0.003 | 0.259 | 0.71 | -0.082 | 0.126 | 0.67 | 0.163 | 1 |
3 | pour | cup | 27 | 0.042 | 0.015 | 0.492 | 0.229 | 0.247 | 0.469 | 0.298 | 1 |
4 | place | tool | 23 | -0.492 | -0.237 | 0.33 | -0.292 | -0.116 | 0.416 | 0.25 | 1 |
5 | place | bottle | 28 | 0.37 | 0.044 | 0.533 | 0.458 | 0.176 | 0.423 | 0.193 | 1 |
6 | insert | cloth | 48 | -0.426 | -0.418 | 0.424 | -0.194 | -0.409 | 0.361 | 0.241 | 1 |
7 | insert | cloth | 26 | 0.268 | -0.013 | 0.275 | 0.488 | 0.135 | 0.111 | 0.312 | 1 |
8 | wipe | bottle | 37 | 0.068 | -0.176 | 0.526 | -0.111 | -0.099 | 0.412 | 0.226 | 1 |
9 | insert | key | 36 | -0.394 | 0.006 | 0.374 | -0.172 | 0.277 | 0.64 | 0.44 | 1 |
10 | place | tool | 44 | -0.228 | -0.363 | 0.423 | -0.12 | -0.364 | 0.352 | 0.13 | 1 |
11 | pick | tool | 35 | -0.46 | -0.422 | 0.563 | -0.547 | -0.309 | 0.817 | 0.292 | 1 |
12 | place | cloth | 39 | 0.011 | 0.484 | 0.653 | 0.08 | 0.406 | 0.503 | 0.182 | 1 |
13 | pour | cloth | 42 | -0.426 | 0.483 | 0.76 | -0.574 | 0.334 | 1.044 | 0.354 | 1 |
14 | insert | tool | 41 | 0.091 | 0.124 | 0.232 | 0.351 | 0.093 | 0.242 | 0.262 | 1 |
15 | pick | cloth | 31 | -0.079 | 0.204 | 0.486 | -0.358 | 0.222 | 0.298 | 0.337 | 1 |
16 | pick | tool | 29 | -0.321 | -0.018 | 0.486 | -0.057 | -0.073 | 0.599 | 0.292 | 1 |
17 | wipe | key | 44 | -0.144 | 0.389 | 0.401 | 0.114 | 0.105 | 0.304 | 0.396 | 1 |
18 | wipe | key | 29 | -0.131 | -0.307 | 0.794 | 0.057 | -0.085 | 1.037 | 0.378 | 1 |
19 | pick | tool | 37 | -0.445 | 0.326 | 0.251 | -0.42 | 0.511 | 0.024 | 0.294 | 0 |
20 | place | tool | 20 | 0.222 | -0.169 | 0.61 | 0.425 | 0.007 | 0.666 | 0.274 | 1 |
21 | insert | tool | 21 | 0.278 | -0.362 | 0.277 | 0.141 | -0.476 | 0.554 | 0.33 | 1 |
22 | pour | key | 21 | -0.346 | 0.114 | 0.33 | -0.615 | 0.026 | 0.269 | 0.289 | 1 |
23 | place | cup | 36 | -0.388 | 0.397 | 0.35 | -0.12 | 0.183 | 0.132 | 0.407 | 1 |
24 | place | key | 27 | 0.366 | -0.151 | 0.538 | 0.16 | 0.028 | 0.387 | 0.312 | 0 |
25 | insert | bottle | 28 | -0.35 | -0.333 | 0.611 | -0.26 | -0.056 | 0.401 | 0.358 | 1 |
26 | insert | key | 31 | -0.062 | -0.22 | 0.72 | -0.287 | -0.274 | 0.78 | 0.24 | 1 |
27 | pour | key | 25 | -0.021 | 0.253 | 0.245 | 0.241 | 0.36 | 0.315 | 0.291 | 1 |
28 | pour | cup | 34 | 0.031 | 0.178 | 0.685 | 0.12 | 0.119 | 0.669 | 0.108 | 1 |
29 | pour | key | 33 | -0.377 | -0.129 | 0.388 | -0.207 | -0.428 | 0.191 | 0.396 | 1 |
30 | insert | key | 37 | -0.1 | -0 | 0.625 | 0.006 | -0.258 | 0.348 | 0.393 | 1 |
31 | pour | key | 49 | 0.098 | -0.192 | 0.5 | 0.077 | -0.128 | 0.332 | 0.182 | 1 |
32 | wipe | key | 49 | -0.305 | -0.084 | 0.586 | -0.031 | -0.038 | 0.761 | 0.328 | 1 |
33 | pick | key | 23 | 0.194 | -0.259 | 0.364 | 0.437 | 0.037 | 0.423 | 0.387 | 1 |
34 | pour | cup | 46 | -0.327 | 0.494 | 0.307 | -0.058 | 0.389 | 0.404 | 0.305 | 1 |
35 | wipe | bottle | 29 | -0.174 | -0.143 | 0.651 | -0.032 | -0.339 | 0.786 | 0.278 | 0 |
36 | insert | cup | 43 | -0.051 | 0.443 | 0.671 | 0.017 | 0.428 | 0.42 | 0.26 | 1 |
37 | place | cloth | 45 | 0.294 | 0.034 | 0.504 | 0.284 | -0.026 | 0.682 | 0.188 | 1 |
38 | wipe | tool | 42 | 0.484 | 0.323 | 0.262 | 0.195 | 0.268 | 0.029 | 0.376 | 1 |
39 | place | cloth | 20 | -0.203 | -0.31 | 0.381 | -0.271 | -0.608 | 0.568 | 0.358 | 1 |
40 | place | cloth | 31 | -0.33 | 0.129 | 0.57 | -0.133 | -0.129 | 0.857 | 0.434 | 1 |
41 | wipe | bottle | 40 | -0.47 | 0.197 | 0.516 | -0.454 | 0.198 | 0.387 | 0.13 | 1 |
42 | pour | bottle | 34 | 0.167 | 0.371 | 0.507 | 0.463 | 0.195 | 0.749 | 0.42 | 0 |
43 | insert | bottle | 49 | -0.221 | -0.086 | 0.441 | -0.072 | -0.001 | 0.218 | 0.281 | 1 |
44 | pour | bottle | 42 | 0.246 | 0.013 | 0.758 | 0.393 | -0.153 | 0.613 | 0.266 | 1 |
45 | place | key | 41 | 0.145 | -0.448 | 0.776 | 0.163 | -0.687 | 0.725 | 0.245 | 1 |
46 | wipe | key | 32 | -0.018 | 0.438 | 0.683 | 0.247 | 0.239 | 0.486 | 0.386 | 0 |
47 | insert | tool | 48 | 0.496 | 0.228 | 0.552 | 0.199 | 0.343 | 0.733 | 0.366 | 1 |
48 | pour | bottle | 36 | 0.389 | 0.002 | 0.364 | 0.222 | 0.18 | 0.49 | 0.275 | 1 |
49 | place | cup | 27 | 0.156 | 0.063 | 0.399 | -0.007 | 0.21 | 0.681 | 0.358 | 1 |
50 | insert | cloth | 21 | -0.062 | 0.426 | 0.571 | 0.114 | 0.544 | 0.86 | 0.359 | 1 |
51 | place | tool | 44 | -0.257 | 0.206 | 0.475 | -0.313 | 0.167 | 0.482 | 0.069 | 1 |
52 | insert | bottle | 26 | -0.055 | -0.361 | 0.272 | 0.119 | -0.316 | 0.373 | 0.206 | 1 |
53 | place | tool | 35 | 0.017 | -0.115 | 0.461 | 0.209 | -0.393 | 0.301 | 0.374 | 1 |
54 | pour | key | 34 | -0.067 | 0.388 | 0.611 | -0.005 | 0.172 | 0.604 | 0.225 | 1 |
55 | place | key | 35 | -0.337 | -0.222 | 0.765 | -0.422 | -0.508 | 0.497 | 0.401 | 1 |
56 | pour | cup | 43 | 0.04 | 0.281 | 0.318 | -0.176 | 0.01 | 0.204 | 0.365 | 1 |
57 | wipe | key | 24 | 0.455 | 0.08 | 0.227 | 0.633 | 0.258 | 0.045 | 0.311 | 1 |
58 | wipe | cloth | 45 | 0.215 | 0.265 | 0.331 | -0.064 | -0.034 | 0.398 | 0.415 | 1 |
59 | insert | key | 23 | -0.243 | 0.342 | 0.332 | -0.224 | 0.206 | 0.605 | 0.306 | 1 |
60 | pour | cloth | 24 | -0.474 | 0.255 | 0.536 | -0.447 | 0.287 | 0.523 | 0.043 | 1 |
61 | pour | tool | 27 | 0.095 | 0.491 | 0.618 | 0.059 | 0.348 | 0.732 | 0.187 | 1 |
62 | insert | key | 29 | -0.298 | -0.251 | 0.254 | -0.396 | -0.455 | 0.445 | 0.296 | 1 |
63 | insert | bottle | 31 | -0.349 | 0.268 | 0.757 | -0.049 | 0.419 | 0.895 | 0.363 | 1 |
64 | place | bottle | 28 | -0.265 | 0.035 | 0.278 | -0.178 | 0.309 | 0.562 | 0.404 | 1 |
65 | pick | bottle | 33 | 0.468 | 0.455 | 0.767 | 0.402 | 0.341 | 0.807 | 0.138 | 1 |
66 | place | cup | 21 | 0.31 | -0.358 | 0.761 | 0.236 | -0.285 | 0.496 | 0.285 | 1 |
67 | pick | bottle | 23 | -0.273 | -0.385 | 0.354 | -0.434 | -0.148 | 0.108 | 0.377 | 1 |
68 | insert | key | 31 | -0.002 | 0.181 | 0.512 | 0.051 | 0.31 | 0.449 | 0.154 | 1 |
69 | place | tool | 24 | -0.213 | -0.143 | 0.252 | -0.471 | 0.028 | 0.106 | 0.342 | 1 |
70 | place | tool | 21 | 0.479 | 0.09 | 0.421 | 0.684 | 0.308 | 0.506 | 0.311 | 0 |
71 | pick | cup | 41 | 0.045 | 0.14 | 0.69 | -0.09 | 0.147 | 0.804 | 0.178 | 1 |
72 | wipe | bottle | 33 | -0.022 | -0.37 | 0.797 | -0.129 | -0.634 | 0.889 | 0.3 | 1 |
73 | place | tool | 38 | 0.341 | -0.298 | 0.327 | 0.499 | -0.468 | 0.356 | 0.234 | 1 |
74 | insert | cup | 32 | 0.37 | 0.368 | 0.703 | 0.54 | 0.163 | 0.953 | 0.366 | 1 |
75 | insert | cup | 41 | 0.121 | 0.41 | 0.662 | 0.398 | 0.476 | 0.715 | 0.29 | 0 |
76 | wipe | tool | 39 | 0.034 | 0.291 | 0.438 | -0.265 | 0.128 | 0.693 | 0.426 | 1 |
77 | place | bottle | 20 | -0.458 | -0.063 | 0.735 | -0.532 | -0.33 | 0.651 | 0.29 | 1 |
78 | wipe | cloth | 37 | 0.028 | 0.328 | 0.327 | 0.053 | 0.113 | 0.172 | 0.267 | 1 |
79 | insert | cup | 20 | 0.376 | -0.173 | 0.671 | 0.475 | -0.232 | 0.596 | 0.137 | 1 |
80 | wipe | cup | 28 | -0.022 | 0.274 | 0.576 | -0.305 | 0.383 | 0.31 | 0.403 | 1 |
81 | place | key | 30 | -0.131 | -0.263 | 0.674 | -0.388 | -0.268 | 0.517 | 0.301 | 1 |
82 | pick | cloth | 34 | -0.387 | -0.035 | 0.586 | -0.428 | -0.236 | 0.665 | 0.219 | 1 |
83 | place | bottle | 40 | -0.396 | 0.329 | 0.48 | -0.475 | 0.572 | 0.369 | 0.279 | 1 |
84 | pour | tool | 35 | -0.232 | -0.111 | 0.519 | -0.026 | 0.168 | 0.568 | 0.35 | 1 |
85 | pour | key | 26 | -0.065 | -0.279 | 0.366 | -0.333 | -0.11 | 0.662 | 0.434 | 1 |
86 | place | tool | 25 | -0.421 | 0.359 | 0.627 | -0.273 | 0.585 | 0.811 | 0.327 | 1 |
87 | place | cup | 24 | -0.187 | -0.31 | 0.536 | -0.214 | -0.419 | 0.288 | 0.271 | 0 |
88 | insert | cloth | 38 | -0.481 | -0.475 | 0.264 | -0.548 | -0.226 | 0.257 | 0.258 | 1 |
89 | insert | cloth | 30 | -0.198 | 0.047 | 0.39 | -0.45 | 0.009 | 0.478 | 0.269 | 1 |
90 | insert | bottle | 28 | 0.283 | -0.297 | 0.464 | 0.161 | -0.12 | 0.39 | 0.227 | 0 |
91 | insert | cup | 37 | 0.486 | -0.205 | 0.212 | 0.704 | -0.37 | 0.497 | 0.395 | 1 |
92 | insert | key | 39 | 0.042 | 0.453 | 0.492 | -0.04 | 0.552 | 0.2 | 0.32 | 1 |
93 | pick | cloth | 24 | -0.38 | -0.346 | 0.414 | -0.593 | -0.179 | 0.638 | 0.351 | 1 |
94 | pour | cloth | 21 | 0.178 | -0.372 | 0.398 | 0.449 | -0.153 | 0.635 | 0.422 | 1 |
95 | pick | cup | 24 | 0.235 | -0.11 | 0.525 | 0.217 | -0.164 | 0.263 | 0.268 | 0 |
96 | insert | cup | 32 | -0.42 | 0.482 | 0.254 | -0.39 | 0.236 | 0.54 | 0.379 | 1 |
97 | pick | bottle | 40 | -0.067 | -0.262 | 0.33 | -0.134 | -0.306 | 0.286 | 0.092 | 1 |
98 | pick | key | 43 | -0.3 | 0.381 | 0.707 | -0.428 | 0.513 | 0.567 | 0.231 | 1 |
99 | pick | key | 49 | 0.223 | 0.267 | 0.266 | 0.158 | 0.486 | 0.412 | 0.272 | 1 |
End of preview.
ACCESS REQUIREMENT - FOLLOW TO DOWNLOAD
This dataset requires following the author to access.
How to Access
- Follow @shangshang on HuggingFace: https://huggingface.co/shangshang
- Request access by commenting on the dataset page
- Once approved, you will receive download permissions
Usage Agreement
- For research and educational purposes only
- Do not redistribute without permission
- Cite the dataset in your work:
@misc{shangshang_dataset_2026,
title={Embodied AI and Medical Datasets},
year={2026},
url={https://huggingface.co/datasets/shangshang}
}
Embodied AI Dataset Collection
A comprehensive dataset for Vision-Language-Action (VLA) model training and embodied AI research.
Datasets Included
| Dataset | Samples | Description |
|---|---|---|
| robot_trajectory.csv | 1000 | Robot arm trajectories for various manipulation tasks |
| joint_configurations.csv | 800 | 6-DOF joint angle configurations |
| end_effector_poses.csv | 600 | End-effector pose sequences |
| force_tactile.csv | 500 | Force/tactile sensor feedback |
| language_instructions.csv | 1000 | Natural language task instructions |
| vla_combined_dataset.csv | 500 | Combined VLA training data |
Task Categories
- pick_place: Pick up object and place at target
- push_pull: Push/pull objects
- stacking: Stack objects
- insertion: Insert objects into targets
- turning: Turn knobs/dials
Robot Types
- Franka Emika (7-DOF)
- UR5e (6-DOF)
- WidowX 250 (7-DOF)
- xArm6 (6-DOF)
Usage Example
import pandas as pd
from huggingface_hub import hf_hub_download
# Download
file_path = hf_hub_download(
repo_id="shangshang/embodied-ai-dataset",
filename="vla_combined_dataset.csv"
)
# Load
df = pd.read_csv(file_path)
print(df.head())
License
MIT License
Citation
If you use this dataset in your research, please cite:
@misc{embodied_ai_dataset_2026, title={Embodied AI VLA Dataset}, year={2026}, url={https://huggingface.co/datasets/shangshang/embodied-ai-dataset} }
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