Dataset Viewer
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: TypeError
Message: Couldn't cast array of type
struct<id: string, name: string, hq_country: string, region: string, segment: string, founded: int64, funding_total_usd: int64, last_round: struct<date: string, amount_usd: int64, series: string, lead_investors: list<item: string>>, employees_approx: int64, key_products: list<item: string>, emea_relevance_en: string, emea_relevance_fr: string, notable_en: string, notable_fr: string, sources: list<item: struct<url: string, publisher: string, date: timestamp[s], fields: list<item: string>>>, last_verified: timestamp[s]>
to
{'id': Value('string'), 'name': Value('string'), 'vendor': Value('string'), 'vendor_country': Value('string'), 'type': Value('string'), 'ai_perf': Value('string'), 'power_w': Value('float64'), 'memory': Value('string'), 'price_usd': Value('int64'), 'form_factor': Value('string'), 'availability': Value('string'), 'target_applications': List(Value('string')), 'robots_using': List(Value('string')), 'notable_en': Value('string'), 'notable_fr': Value('string'), 'sources': List({'url': Value('string'), 'publisher': Value('string'), 'date': Value('timestamp[s]'), 'fields': List(Value('string'))}), 'last_verified': Value('timestamp[s]')}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<id: string, name: string, hq_country: string, region: string, segment: string, founded: int64, funding_total_usd: int64, last_round: struct<date: string, amount_usd: int64, series: string, lead_investors: list<item: string>>, employees_approx: int64, key_products: list<item: string>, emea_relevance_en: string, emea_relevance_fr: string, notable_en: string, notable_fr: string, sources: list<item: struct<url: string, publisher: string, date: timestamp[s], fields: list<item: string>>>, last_verified: timestamp[s]>
to
{'id': Value('string'), 'name': Value('string'), 'vendor': Value('string'), 'vendor_country': Value('string'), 'type': Value('string'), 'ai_perf': Value('string'), 'power_w': Value('float64'), 'memory': Value('string'), 'price_usd': Value('int64'), 'form_factor': Value('string'), 'availability': Value('string'), 'target_applications': List(Value('string')), 'robots_using': List(Value('string')), 'notable_en': Value('string'), 'notable_fr': Value('string'), 'sources': List({'url': Value('string'), 'publisher': Value('string'), 'date': Value('timestamp[s]'), 'fields': List(Value('string'))}), 'last_verified': Value('timestamp[s]')}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Physical AI Atlas — open dataset
Bilingual (FR/EN) open dataset covering the physical AI ecosystem: humanoid robots,
robotic platforms, vision-language-action models, embedded AI chips, simulators,
research labs, established companies and startups. Hand-collected and hand-verified
from public sources, with a source (publisher, URL, date) attached to every individual
field rather than one blanket citation per entry, plus a last_verified date per entity.
Rendered as a browsable, bilingual atlas at https://www.d-fairy.fr/atlas/.
Files
Thirteen JSON files, one per dataset:
| File | Content |
|---|---|
robots.json |
Humanoid and mobile robots |
models.json |
Vision-language-action (VLA) models |
chips.json |
Embedded AI compute for robotics |
simulators.json |
Robotics simulators and RL environments |
labs.json |
Research labs |
companies.json |
Established companies |
startups.json |
Startups |
platforms.json |
Robotic platforms |
glossary.json |
Domain glossary |
readiness.json |
Technology readiness notes |
quotes.json |
Sourced quotes |
testimonials.json |
Testimonials |
news.json |
News items |
Schema
Each entity carries id, domain fields (e.g. payload_kg, runtime_h, compute,
availability for robots), sources (list of {url, publisher, date, fields}),
and last_verified.
License
CC BY 4.0. Attribution: cite the Physical AI Atlas with a link back to
https://www.d-fairy.fr/atlas/ or this repository. See CITATION.cff for the
canonical citation form and LICENSE for the full legal text.
Source
Maintained by Christian Verbrugge (D-Fairy Consulting). Canonical source repository: https://github.com/PlbKin190/physical-ai-atlas-data
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