The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label simpleicl-sample@862ab87662dc15ae185fcee94383dcf34fac31ec
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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2474, 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 2391, 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 2192, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1472, 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 1495, 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 1168, in encode_example
example_data = self.str2int(example_data)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1105, in str2int
output = [self._strval2int(value) for value in values]
~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1126, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label simpleicl-sample@862ab87662dc15ae185fcee94383dcf34fac31ecNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
SimpleICL Real-Robot Data Sample
A sample of our paired human-robot manipulation data, released alongside SimpleICL — the official code release of the paper In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks. It is a subset of the full dataset collected for the project, provided to illustrate the data format and to accompany the training recipe in the repository README (Training).
Dataset Summary
100 paired episodes of bimanual Franka FR3 tabletop manipulation. In every
pair, a teleoperated robot arm and a human hand perform the same task back to
back; the human-side demonstration video serves as the in-context condition
(video_ctx) that the policy consumes at training and inference time.
| Episodes | 100 pairs (100 robot + 100 human) |
| Total frames (robot side) | 28,457 @ 30 fps |
| Episode length | 157–449 frames |
| Robot side | 3 RealSense cameras (high + left/right wrist), 848×480 |
| Human side | 1 high camera, 848×480 |
| Action / state | 16-D (7 joints + 1 gripper per arm) |
Scene alterations keep the task unchanged and perturb only task-irrelevant
factors — object positions, lighting, and background colors. The same task
recorded under the base and altered placements references each other as
in-context conditions (see robot/meta/conditions_list.json), so a policy
trained on this data learns what the task is from the demonstration video
while staying robust to where objects sit, how the scene is lit, and what the
background looks like.
Structure
The data is distributed as a single archive, pair_data.zip. Unzipping it
produces two sibling LeRobot-format (v2.1) dataset directories:
pair_data.zip
└── (extracts to:)
├── robot/ # teleoperated robot episodes
│ ├── meta/ # tasks, episodes, scenes, conditions
│ │ ├── tasks.jsonl
│ │ ├── episodes.jsonl
│ │ ├── episodes_stats.jsonl
│ │ ├── info.json
│ │ ├── scenes.json # episode index -> scene placement label
│ │ ├── plan_rows.json
│ │ ├── conditions.json
│ │ └── conditions_list.json # episode index -> human demo videos (video_ctx)
│ ├── data/ # parquet per episode
│ └── videos/ # 3 cameras per episode
└── human/ # human-hand demonstrations
└── videos/ # cam_high video per episode
Condition paths in robot/meta/conditions_list.json are relative to the
extraction root, so the dataset keeps working after the extracted directory
is moved as a whole.
Usage
Download the archive, unzip it, and point the SimpleICL training config at the robot side — see the repository SimpleICL README for the one-command training flow.
Additional Information
- License: MIT
- Origin: a sample subset of the data collected by the SimpleICL authors on a dual-arm Franka FR3 platform with RealSense cameras.
- Associated code: SimpleICL
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