The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label pick_attribute
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 2368, in __iter__
example = _apply_feature_types_on_example(
example, self.features, token_per_repo_id=self.token_per_repo_id
)
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
example_data = self.str2int(example_data)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
output = [self._strval2int(value) for value in values]
~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label pick_attributeNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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Check out the documentation for more information.
InstructMove Dataset
Version
Version 1.0
InstructMove is a language-conditioned robot manipulation dataset in LeRobot format. It contains instruction-following trajectories for object picking, spatially grounded picking, and object placement. The current release includes data collected with PiperX dual-arm and Franka robots.
Dataset Contents
| Dataset | Task | Robot | Path | Episodes |
|---|---|---|---|---|
| Pick by attribute | Pick an object specified by visual attributes such as color, shape, or appearance. | PiperX dual-arm | lerobot_dataset/pick_attribute/piperx_0710.zip |
100 |
| Pick by category | Pick an object specified by its semantic category and visual description. | PiperX dual-arm | lerobot_dataset/pick_category/piperx_0710.zip |
100 |
| Place A to B | Pick a specified object and place it in or on a target container. | PiperX dual-arm | lerobot_dataset/place_a2b/piperx_0710.zip |
100 |
| Place A to B | Pick a specified object and place it in or on a target container. | Franka | lerobot_dataset/place_a2b/franka_0710.zip |
100 |
| Spatial pick | Pick an object identified through a spatial relation to another object, such as left of, right of, near, or far from. | Franka | lerobot_dataset/spatial_pick/franka_0710.zip |
100 |
| Total | lerobot_dataset |
500 |
Dataset Structure
Each row in the table corresponds to an independent LeRobot dataset packaged
as a .zip archive. Each archive contains trajectory data, videos, and
metadata, including episode and task annotations. Episode counts are taken
from each dataset's meta/info.json and verified against
meta/episodes.jsonl.
Only the official LeRobot meta/ files are included:
info.jsonepisodes.jsonltasks.jsonlstats.json
Usage
Each .zip archive is a self-contained LeRobot dataset. Extract the archive
before loading with the LeRobot API:
unzip lerobot_dataset/pick_category/piperx_0710.zip -d pick_category
# then load pick_category/ with LeRobotDataset
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