Datasets:

researchase
/

example_dataset

Tasks:

Robotics

Tags:

phosphobot

so100

phospho-dk

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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:    ArrowInvalid
Message:      Schema at index 1 was different: 
episode_index: int64
tasks: list<item: string>
length: int64
vs
episode_index: int64
stats: struct<observation.state: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, action: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, timestamp: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, frame_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, episode_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, task_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, observation.images.main: struct<max: list<item: list<item: list<item: double>>>, min: list<item: list<item: list<item: double>>>, mean: list<item: list<item: list<item: double>>>, std: list<item: list<item: list<item: double>>>, count: list<item: int64>>>
Traceback:    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 559, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
                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: Schema at index 1 was different: 
              episode_index: int64
              tasks: list<item: string>
              length: int64
              vs
              episode_index: int64
              stats: struct<observation.state: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, action: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, timestamp: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, frame_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, episode_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, task_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, observation.images.main: struct<max: list<item: list<item: list<item: double>>>, min: list<item: list<item: list<item: double>>>, mean: list<item: list<item: list<item: double>>>, std: list<item: list<item: list<item: double>>>, count: list<item: int64>>>

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example_dataset

This dataset was generated using phosphobot.

This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot.

To get started in robotics, get your own phospho starter pack..

Downloads last month: 8

Error code: FeaturesError Exception: ArrowInvalid Message: Schema at index 1 was different: episode_index: int64 tasks: list<item: string> length: int64 vs episode_index: int64 stats: struct<observation.state: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, action: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, timestamp: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, frame_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, episode_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, task_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, observation.images.main: struct<max: list<item: list<item: list<item: double>>>, min: list<item: list<item: list<item: double>>>, mean: list<item: list<item: list<item: double>>>, std: list<item: list<item: list<item: double>>>, count: list<item: int64>>> Traceback: 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 559, in _iter_arrow yield new_key, pa.Table.from_batches(chunks_buffer) File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches 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: Schema at index 1 was different: episode_index: int64 tasks: list<item: string> length: int64 vs episode_index: int64 stats: struct<observation.state: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, action: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, timestamp: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, frame_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, episode_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, task_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, observation.images.main: struct<max: list<item: list<item: list<item: double>>>, min: list<item: list<item: list<item: double>>>, mean: list<item: list<item: list<item: double>>>, std: list<item: list<item: list<item: double>>>, count: list<item: int64>>>