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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ArrowInvalid
Message:      Mismatching child array lengths
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 83, in _generate_tables
                  pa_table = _recursive_load_arrays(h5, self.info.features, start, end)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 267, in _recursive_load_arrays
                  arr = _recursive_load_arrays(obj, features[path], start, end)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 288, in _recursive_load_arrays
                  sarr = pa.StructArray.from_arrays(values, names=keys)
                File "pyarrow/array.pxi", line 4304, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: Mismatching child array lengths

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Image CCIL experiment datasets

Datasets used by the Image CCIL PushT and Robomimic experiments.

Path Task Episodes Steps Notes
pusht/pusht_cchi_v7_replay_41.zarr/ PushT 41 5,216 Experiment dataset
can/can_ph_demo_v141_20_perc.hdf5 Can 40 4,542 20% source subset
square/square_abs_ph_demo_v141_20_perc.hdf5 Square 40 6,010 20% source subset
tool_hang/tool_hang_all_success_ph_demo_v141_20_perc.hdf5 ToolHang 40 18,100 20% source subset
transport/transport_ph_demo_v141_20_perc.hdf5 Transport 40 19,045 20% source subset

The Robomimic 10% experiments select the first 20 demonstrations at load time from these 40-trajectory files (for example, with dataset.num_traj=20 or --max_episodes 20). The files themselves contain all 40 trajectories listed above.

PushT is stored as an unpacked Zarr directory. The Robomimic tasks are stored as HDF5 files and retain the observation, action, and environment metadata used by the training and fixed-state evaluation pipelines.

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