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mncai
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G1_physical_ai_maps

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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
name: string
version: string
robot: string
sensor: struct<lidar: string>
  child 0, lidar: string
frame: string
points: int64
fields: list<item: string>
  child 0, item: string
data_format: string
bounds: struct<x: list<item: double>, y: list<item: double>, z: list<item: double>>
  child 0, x: list<item: double>
      child 0, item: double
  child 1, y: list<item: double>
      child 0, item: double
  child 2, z: list<item: double>
      child 0, item: double
size_m: struct<x: double, y: double, z: double>
  child 0, x: double
  child 1, y: double
  child 2, z: double
tasks: list<item: string>
  child 0, item: string
md5: struct<map.pcd: string, tasklist.json: string>
  child 0, map.pcd: string
  child 1, tasklist.json: string
count: int64
datasets: list<item: struct<robot: string, map: string, version: string, points: int64, size_m: list<item: dou (... 47 chars omitted)
  child 0, item: struct<robot: string, map: string, version: string, points: int64, size_m: list<item: double>, tasks (... 35 chars omitted)
      child 0, robot: string
      child 1, map: string
      child 2, version: string
      child 3, points: int64
      child 4, size_m: list<item: double>
          child 0, item: double
      child 5, tasks: list<item: string>
          child 0, item: string
      child 6, path: string
to
{'datasets': List({'robot': Value('string'), 'map': Value('string'), 'version': Value('string'), 'points': Value('int64'), 'size_m': List(Value('float64')), 'tasks': List(Value('string')), 'path': Value('string')}), 'count': Value('int64')}
because column names don't match
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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              name: string
              version: string
              robot: string
              sensor: struct<lidar: string>
                child 0, lidar: string
              frame: string
              points: int64
              fields: list<item: string>
                child 0, item: string
              data_format: string
              bounds: struct<x: list<item: double>, y: list<item: double>, z: list<item: double>>
                child 0, x: list<item: double>
                    child 0, item: double
                child 1, y: list<item: double>
                    child 0, item: double
                child 2, z: list<item: double>
                    child 0, item: double
              size_m: struct<x: double, y: double, z: double>
                child 0, x: double
                child 1, y: double
                child 2, z: double
              tasks: list<item: string>
                child 0, item: string
              md5: struct<map.pcd: string, tasklist.json: string>
                child 0, map.pcd: string
                child 1, tasklist.json: string
              count: int64
              datasets: list<item: struct<robot: string, map: string, version: string, points: int64, size_m: list<item: dou (... 47 chars omitted)
                child 0, item: struct<robot: string, map: string, version: string, points: int64, size_m: list<item: double>, tasks (... 35 chars omitted)
                    child 0, robot: string
                    child 1, map: string
                    child 2, version: string
                    child 3, points: int64
                    child 4, size_m: list<item: double>
                        child 0, item: double
                    child 5, tasks: list<item: string>
                        child 0, item: string
                    child 6, path: string
              to
              {'datasets': List({'robot': Value('string'), 'map': Value('string'), 'version': Value('string'), 'points': Value('int64'), 'size_m': List(Value('float64')), 'tasks': List(Value('string')), 'path': Value('string')}), 'count': Value('int64')}
              because column names don't match

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G1 Physical AI Maps

3D point cloud maps and navigation goals collected with a Unitree G1.

map	points	size (m)	tasks
`lab`	19,124	12.8 x 12.4 x 3.4	goal_1 ~ goal_4

Layout

maps/g1/lab/
  map.pcd          point cloud (PCD binary; x y z intensity normal_x normal_y normal_z curvature)
  tasklist.json    goal poses (position + quaternion in the map frame; array order is registration order)
  metadata.json    bounds, md5

Usage

from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "mncai/G1_physical_ai_maps",
    "maps/g1/lab/map.pcd",
    repo_type="dataset",
    revision="map-g1-lab-v1",   # omit for latest
)

Map versions are pinned with git tags.
Verify downloads against the md5 values in metadata.json.
Coordinates are in the map frame, gravity aligned and z-up, so no extra transform is needed.
The HF dataset viewer cannot render .pcd, so 3D inspection is done in a separate Space.

Downloads last month: 23

Total file size:

618 kB