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GRN_datasets

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train

The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ArrowTypeError
Message:      ("Expected bytes, got a 'int' object", 'Conversion failed for column Front_IC with type object')
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 151, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                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.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 499, in _iter_arrow
                  for key, pa_table in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 346, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 181, in _generate_tables
                  pa_table = pa.Table.from_pandas(df, preserve_index=False)
                File "pyarrow/table.pxi", line 3874, in pyarrow.lib.Table.from_pandas
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 624, in dataframe_to_arrays
                  arrays[i] = maybe_fut.result()
                File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 439, in result
                  return self.__get_result()
                File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 391, in __get_result
                  raise self._exception
                File "/usr/local/lib/python3.9/concurrent/futures/thread.py", line 58, in run
                  result = self.fn(*self.args, **self.kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 598, in convert_column
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 592, in convert_column
                  result = pa.array(col, type=type_, from_pandas=True, safe=safe)
                File "pyarrow/array.pxi", line 339, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 85, in pyarrow.lib._ndarray_to_array
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'int' object", 'Conversion failed for column Front_IC with type object')

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GRN Datasets

Dataset Summary

Dataset name: GRN Datasets
Task: Geometric feasibility prediction for robot manipulation
Domain: Robot Task and Motion Planning (TAMP)
Data source: Physics-based simulation
License: See repository / Hugging Face dataset page
Maintainer: Smail Ait Bouhsain

The GRN datasets consist of large-scale simulated manipulation scenes annotated with feasibility labels for inverse kinematics, grasp obstruction, and action/grasp feasibility.

Dataset Structure

In-Distribution Datasets

panda_3d_4
panda_tabletop_4
pr2_3d_4

Out-of-Distribution Test Datasets

panda_3d_10
panda_3d_15
panda_3d_20

The OOD datasets increase the number of objects per scene and their size range to evaluate generalization under heavy clutter and unseen object dimensions.

Data Generation Process

Procedurally generated scenes in simulation
Each scene contains:
- A robot (Panda or PR2)
- Multiple rigid objects with 3D geometry
Feasibility labels computed using:
- Grasp sampling
- Inverse kinematics solvers
- Collision checking
- Motion planning validation

All annotations are generated automatically; no human labeling is involved.

Data Format

Each dataset is split into train, val, and test.

Each split contains:

scenes/*.json: raw scene descriptions
data/data_annotated.json: feasibility and infeasibility cause annotations per object per scene
scenes_info.json: metadata per seen (Scene ID, Number of fixed/movable objects and obstacles)
data_generation_parameters.json: parameters used during scene generation (number of objects range, object size range, etc)

Supported Tasks

Inverse kinematics feasibility prediction
Grasp obstruction prediction
Action and grasp feasibility prediction

Intended Use

Primary Use

Training action and grasp feasibility predictors
Benchmarking geometric reasoning models
Research on generalization in TAMP

Out-of-Scope Use

Direct real-world deployment without validation
Non-robotic applications

Biases and Limitations

Simulation-only data
Limited robot platforms and object assets
Does not capture full real-world sensing noise

Ethical Considerations

The dataset contains no personal data or human subjects and is suitable for open academic research.

Citation

@inproceedings{ait2025learning,
  title={Learning Geometric Reasoning Networks for Robot Task and Motion Planning},
  author={Ait Bouhsain, Smail and Alami, Rachid and Simeon, Thierry},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025}
}

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