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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    TypeError
Message:      int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1531, in _prepare_split_single
                  for key, record in generator:
                                     ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 127, in _generate_examples
                  for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
                                              ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  for filename, f in tar_iterator:
                                     ^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
                  for x in self.generator(*self.args):
                           ~~~~~~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1400, in _iter_from_urlpath
                  with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
                       ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 977, in xopen
                  file_obj = fs.open(paths[0], mode)
                File "<string>", line 3, in open
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
                  return self._mock_call(*args, **kwargs)
                         ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
                  return self._execute_mock_call(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
                  result = effect(*args, **kwargs)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
                  tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
                                                               ~~~^^^^^^^^
              TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
              
              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 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 1393, 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 1571, 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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pkl
unknown
__key__
string
__url__
string
[ 128, 4, 149, 178, 7, 1, 0, 0, 0, 0, 0, 140, 28, 112, 101, 114, 112, 101, 116, 117, 97, 50, 46, 117, 116, 105, 108, 115, 46, 102, 105, 108, 116, 101, 114, 95, 115, 116, 97, 116, 101, 148, 140, 6, 79, 98, 106, 101, 99, 1...
estimators/scene_153/AlarmClock_surface_6_11_object
hf://datasets/Mycroft96/predictive-graphs-procthor-153@d30ebc1fda3aba239cdf8749b919a85e4fe0c12d/predictive-graphs-procthor-153-demo.tar.gz
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estimators/scene_153/Apple_surface_8_25_object
"hf://datasets/Mycroft96/predictive-graphs-procthor-153@d30ebc1fda3aba239cdf8749b919a85e4fe0c12d/pre(...TRUNCATED)
"gASVAwgBAAAAAACMHHBlcnBldHVhMi51dGlscy5maWx0ZXJfc3RhdGWUjAZPYmplY3SUk5QpgZR9lCiMBnN0YXRlc5RdlChoAIw(...TRUNCATED)
estimators/scene_153/BasketBall_4_2_object
"hf://datasets/Mycroft96/predictive-graphs-procthor-153@d30ebc1fda3aba239cdf8749b919a85e4fe0c12d/pre(...TRUNCATED)
"gASVg3UAAAAAAACMHHBlcnBldHVhMi51dGlscy5maWx0ZXJfc3RhdGWUjAZPYmplY3SUk5QpgZR9lCiMBnN0YXRlc5RdlGgAjBF(...TRUNCATED)
estimators/scene_153/Book_surface_6_12_object
"hf://datasets/Mycroft96/predictive-graphs-procthor-153@d30ebc1fda3aba239cdf8749b919a85e4fe0c12d/pre(...TRUNCATED)
"gASVrHUAAAAAAACMHHBlcnBldHVhMi51dGlscy5maWx0ZXJfc3RhdGWUjAZPYmplY3SUk5QpgZR9lCiMBnN0YXRlc5RdlGgAjBF(...TRUNCATED)
estimators/scene_153/Candle_surface_7_16_object
"hf://datasets/Mycroft96/predictive-graphs-procthor-153@d30ebc1fda3aba239cdf8749b919a85e4fe0c12d/pre(...TRUNCATED)
"gASVFggBAAAAAACMHHBlcnBldHVhMi51dGlscy5maWx0ZXJfc3RhdGWUjAZPYmplY3SUk5QpgZR9lCiMBnN0YXRlc5RdlChoAIw(...TRUNCATED)
estimators/scene_153/Egg_surface_8_19_object
"hf://datasets/Mycroft96/predictive-graphs-procthor-153@d30ebc1fda3aba239cdf8749b919a85e4fe0c12d/pre(...TRUNCATED)
"gASVhXUAAAAAAACMHHBlcnBldHVhMi51dGlscy5maWx0ZXJfc3RhdGWUjAZPYmplY3SUk5QpgZR9lCiMBnN0YXRlc5RdlGgAjBF(...TRUNCATED)
estimators/scene_153/Laptop_surface_6_13_object
"hf://datasets/Mycroft96/predictive-graphs-procthor-153@d30ebc1fda3aba239cdf8749b919a85e4fe0c12d/pre(...TRUNCATED)
"gASV3gcBAAAAAACMHHBlcnBldHVhMi51dGlscy5maWx0ZXJfc3RhdGWUjAZPYmplY3SUk5QpgZR9lCiMBnN0YXRlc5RdlChoAIw(...TRUNCATED)
estimators/scene_153/Pen_surface_4_6_object
"hf://datasets/Mycroft96/predictive-graphs-procthor-153@d30ebc1fda3aba239cdf8749b919a85e4fe0c12d/pre(...TRUNCATED)
"gASV8QcBAAAAAACMHHBlcnBldHVhMi51dGlscy5maWx0ZXJfc3RhdGWUjAZPYmplY3SUk5QpgZR9lCiMBnN0YXRlc5RdlChoAIw(...TRUNCATED)
estimators/scene_153/Potato_surface_8_23_object
"hf://datasets/Mycroft96/predictive-graphs-procthor-153@d30ebc1fda3aba239cdf8749b919a85e4fe0c12d/pre(...TRUNCATED)
"gASVinUAAAAAAACMHHBlcnBldHVhMi51dGlscy5maWx0ZXJfc3RhdGWUjAZPYmplY3SUk5QpgZR9lCiMBnN0YXRlc5RdlGgAjBF(...TRUNCATED)
estimators/scene_153/RemoteControl_surface_4_5_object
"hf://datasets/Mycroft96/predictive-graphs-procthor-153@d30ebc1fda3aba239cdf8749b919a85e4fe0c12d/pre(...TRUNCATED)
End of preview.

Predictive Graphs: SemiStaticSim example data (ProcTHOR house 153)

Example data for Predictive Graphs: one SemiStaticSim house (ProcTHOR-10k test layout 153) simulated over four weeks (672 hours), with fitted Perpetua* estimators and a Predictive Graph built at hour 504.

Files

Archive Size Contents Needed for
predictive-graphs-procthor-153-demo.tar.gz 147 MB Map, estimators, object schedules, and the observations of all 672 hourly runs (no images) Navigation demo, map viewer, fitting estimators
predictive-graphs-procthor-153-rgbd.tar.gz 2.0 GB RGB-D frames and poses of runs 485-504 Building a map

Download

From the root of the Predictive Graphs repository:

hf download Mycroft96/predictive-graphs-procthor-153 --repo-type dataset --local-dir data/archives
mkdir -p data
tar -xzf data/archives/predictive-graphs-procthor-153-demo.tar.gz -C data
tar -xzf data/archives/predictive-graphs-procthor-153-rgbd.tar.gz -C data

Layout

data/
β”œβ”€β”€ semistaticsim/test/153/0/
β”‚   β”œβ”€β”€ groundtruth/            # where each of the 15 pickupables is at every hour
β”‚   └── privileged/run_<hour>/  # one robot run per hour: observations, config; RGB-D for hours 485-504
β”œβ”€β”€ estimators/scene_153/       # one Perpetua* estimator per pickupable
└── maps/scene_153/             # Predictive Graph built at hour 504

Each run_<hour> holds the robot's observations of which objects it saw on which receptacles (scan_0.parquet), the receptacles' bounding boxes and the run configuration. Runs 485-504 also hold images/ (rgb_first_view/, depth/, semantics/) and poses/ (one camera pose per frame).

Source and license

The houses come from ProcTHOR-10k and are rendered with AI2-THOR, both released under the Apache-2.0 license. This dataset is released under CC BY 4.0.

Citation

@article{saavedra2026predictive,
  title   = {Predictive Spatio-Temporal Scene Graphs for Semi-Static Scenes},
  author  = {Saavedra-Ruiz, Miguel and Gauthier, Charlie and Gupta, Kumaraditya and Shahfar, Shima and Ellis, Kirsty and Parkison, Steven and Paull, Liam},
  journal = {IEEE Robotics and Automation Letters},
  year    = {2026}
}
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