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MolmoAct2-so101-pickorange-testvector

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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:    CastError
Message:      Couldn't cast
sample_id: string
episode_index: int64
frame_index: int64
timestamp: double
task: string
input_json: string
output_json: string
front_png: string
wrist_png: string
input_npz: string
output_npz: string
state: list<item: double>
  child 0, item: double
first_action: list<item: double>
  child 0, item: double
created_at: string
source_dataset: string
output_contract: struct<actions_shape: list<item: int64>, action_names: list<item: string>, action_frame: string>
  child 0, actions_shape: list<item: int64>
      child 0, item: int64
  child 1, action_names: list<item: string>
      child 0, item: string
  child 2, action_frame: string
num_samples: int64
horizon: int64
source_revision: string
input_contract: struct<images: list<item: string>, state_shape: list<item: int64>, language: string>
  child 0, images: list<item: string>
      child 0, item: string
  child 1, state_shape: list<item: int64>
      child 0, item: int64
  child 2, language: string
samples: list<item: struct<sample_id: string, episode_index: int64, frame_index: int64, timestamp: double, ta (... 191 chars omitted)
  child 0, item: struct<sample_id: string, episode_index: int64, frame_index: int64, timestamp: double, task: string, (... 179 chars omitted)
      child 0, sample_id: string
      child 1, episode_index: int64
      child 2, frame_index: int64
      child 3, timestamp: double
      child 4, task: string
      child 5, input_json: string
      child 6, output_json: string
      child 7, front_png: string
      child 8, wrist_png: string
      child 9, input_npz: string
      child 10, output_npz: string
      child 11, state: list<item: double>
          child 0, item: double
      child 12, first_action: list<item: double>
          child 0, item: double
to
{'created_at': Value('string'), 'source_dataset': Value('string'), 'source_revision': Value('string'), 'num_samples': Value('int64'), 'frame_index': Value('int64'), 'horizon': Value('int64'), 'input_contract': {'images': List(Value('string')), 'state_shape': List(Value('int64')), 'language': Value('string')}, 'output_contract': {'actions_shape': List(Value('int64')), 'action_names': List(Value('string')), 'action_frame': Value('string')}, 'samples': List({'sample_id': Value('string'), 'episode_index': Value('int64'), 'frame_index': Value('int64'), 'timestamp': Value('float64'), 'task': Value('string'), 'input_json': Value('string'), 'output_json': Value('string'), 'front_png': Value('string'), 'wrist_png': Value('string'), 'input_npz': Value('string'), 'output_npz': Value('string'), 'state': List(Value('float64')), 'first_action': List(Value('float64'))})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              sample_id: string
              episode_index: int64
              frame_index: int64
              timestamp: double
              task: string
              input_json: string
              output_json: string
              front_png: string
              wrist_png: string
              input_npz: string
              output_npz: string
              state: list<item: double>
                child 0, item: double
              first_action: list<item: double>
                child 0, item: double
              created_at: string
              source_dataset: string
              output_contract: struct<actions_shape: list<item: int64>, action_names: list<item: string>, action_frame: string>
                child 0, actions_shape: list<item: int64>
                    child 0, item: int64
                child 1, action_names: list<item: string>
                    child 0, item: string
                child 2, action_frame: string
              num_samples: int64
              horizon: int64
              source_revision: string
              input_contract: struct<images: list<item: string>, state_shape: list<item: int64>, language: string>
                child 0, images: list<item: string>
                    child 0, item: string
                child 1, state_shape: list<item: int64>
                    child 0, item: int64
                child 2, language: string
              samples: list<item: struct<sample_id: string, episode_index: int64, frame_index: int64, timestamp: double, ta (... 191 chars omitted)
                child 0, item: struct<sample_id: string, episode_index: int64, frame_index: int64, timestamp: double, task: string, (... 179 chars omitted)
                    child 0, sample_id: string
                    child 1, episode_index: int64
                    child 2, frame_index: int64
                    child 3, timestamp: double
                    child 4, task: string
                    child 5, input_json: string
                    child 6, output_json: string
                    child 7, front_png: string
                    child 8, wrist_png: string
                    child 9, input_npz: string
                    child 10, output_npz: string
                    child 11, state: list<item: double>
                        child 0, item: double
                    child 12, first_action: list<item: double>
                        child 0, item: double
              to
              {'created_at': Value('string'), 'source_dataset': Value('string'), 'source_revision': Value('string'), 'num_samples': Value('int64'), 'frame_index': Value('int64'), 'horizon': Value('int64'), 'input_contract': {'images': List(Value('string')), 'state_shape': List(Value('int64')), 'language': Value('string')}, 'output_contract': {'actions_shape': List(Value('int64')), 'action_names': List(Value('string')), 'action_frame': Value('string')}, 'samples': List({'sample_id': Value('string'), 'episode_index': Value('int64'), 'frame_index': Value('int64'), 'timestamp': Value('float64'), 'task': Value('string'), 'input_json': Value('string'), 'output_json': Value('string'), 'front_png': Value('string'), 'wrist_png': Value('string'), 'input_npz': Value('string'), 'output_npz': Value('string'), 'state': List(Value('float64')), 'first_action': List(Value('float64'))})}
              because column names don't match

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YAML Metadata Warning:empty or missing yaml metadata in repo card

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MolmoAct2 SO101 PickOrange Test Vectors

This repository contains 5 input/output test vectors extracted from LightwheelAI/leisaac-pick-orange at revision fa6e0625d814352b8e6ee1c6d2482194e4da8ed3.

Each sample is structured for the MolmoAct2 SO101 evaluation path:

  • input: front RGB image, wrist RGB image, 6D observation.state, and task text
  • output: 30-step action chunk with shape [30, 6]
  • action semantics: SO101 absolute joint-pose targets in motor-degree scale

The source dataset task string is:

Grab orange and place into plate

Files

  • manifest.json: full machine-readable manifest
  • samples.jsonl: one-line summary per sample
  • samples/sample_XXX/input.json: policy request schema with image file references
  • samples/sample_XXX/output.json: expected action chunk
  • samples/sample_XXX/input.npz: exact input arrays
  • samples/sample_XXX/output.npz: exact output arrays
  • scripts/extract_pickorange_testvectors.py: extraction script used to generate this repo

Reproduce 

HF_TOKEN=<token> python scripts/extract_pickorange_testvectors.py \
  --source-repo LightwheelAI/leisaac-pick-orange \
  --output-dir ./MolmoAct2-so101-pickorange-testvector \
  --episodes 0,15,30,45,59 \
  --frame-index 0 \
  --horizon 30 \
  --force
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