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aboguszewski
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robomimic

Tasks:

Robotics

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1K<n<10K

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train

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 478, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 87, 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 273, in _recursive_load_arrays
                  arr = _recursive_load_arrays(dset, features[path], start, end)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 294, in _recursive_load_arrays
                  sarr = pa.StructArray.from_arrays(values, names=keys)
                File "pyarrow/array.pxi", line 4306, 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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robomimic

Dataset Summary

This dataset contains demonstrations of robosuite tasks, separated into two directories: images and dino3-embeddings.
The demonstrations in both of the directories are exactly the same. They differ by the format in which they store camera observations. images stores them as images, where dino3-embeddings stores the embeddings of those images, outputed by the small variant of the DINOv3 model.

The dataset uses robotview and eye_in_hand camera positions. It also records rewards.

Block lifting and nut assembly demonstrations were sourced from the robomimic dataset.
Block stacking demonstrations are original and were collected via robosuite's data collection script on a keyboard.
All IIWA arm demonstrations are also original and collected in the same way.
The machine generated dataset for block lifting is originally much larger than the samples available here. The ones here are the last 200 from the original robomimic dataset.

DINOv3 is an open-weights image embedding model.

For more information about the original dataset see the robomimic docs.

Details

File	Robot	Policy	Rewards	Image Resolution	# of Demonstrations	Mean Trajectory Length (approx)
block-lifting/panda/ph	Panda arm	proficient human	dense	512x512	200	48
block-lifting/panda/mh-better	Panda arm	"better" human operator	dense	512x512	100	72
block-lifting/panda/mh-okay	Panda arm	"okay" human operator	dense	512x512	100	93
block-lifting/panda/mh-worse	Panda arm	"worse" human operator	dense	512x512	100	145
block-lifting/panda/mg	Panda arm	machine generated	dense	512x512	200	150
block-lifting/iiwa/kh	IIWA arm	keyboard human	dense	512x512	11	54
nut-assembly/panda/ph	Panda arm	proficient human	dense	512x512	200	150
nut-assembly/panda/mh-better	Panda arm	"better" human operator	dense	512x512	50	216
nut-assembly/panda/mh-okay	Panda arm	"okay" human operator	dense	512x512	100	264
nut-assembly/panda/mh-worse	Panda arm	"worse" human operator	dense	512x512	100	357
block-stacking/panda/kh	Panda arm	keyboard human	dense	512x512	10	99
block-stacking/iiwa/kh	IIWA arm	keyboard human	dense	512x512	11	72

DINOv3 embeddings are 384-dimensional.

For more information about the tasks and environment see:

For more information about the robots see robosuite/manipulators.

Notes

Mean trajectory length within a task is a decent metric of demonstration quality. As a rule of thumb ph ~ mh-better > mh-okay > mh-worse > mg.
Machine generated trajectories are really weird. Not even remotely human-looking.
Machine generated trajectories are not always succesful and when they are, they do not terminate after the success. They go on for 150 steps every time.
Original robomimic datasets for nut assembly (square) had duplicated demonstrations. One of the operators' demonstrations from the mh-better dataset were also used for the ph dataset. They are deduplicated here, so that's the reason why nut assembly mh-better contains 50 demonstrations instead of a 100.