The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
auditor_sha256: string
cases: list<item: struct<ablations_ever_accept: struct<any_instance_only: bool, body_origin_only: bool, geo (... 190 chars omitted)
child 0, item: struct<ablations_ever_accept: struct<any_instance_only: bool, body_origin_only: bool, geometry_only: (... 178 chars omitted)
child 0, ablations_ever_accept: struct<any_instance_only: bool, body_origin_only: bool, geometry_only: bool, without_dwell: bool, wi (... 18 chars omitted)
child 0, any_instance_only: bool
child 1, body_origin_only: bool
child 2, geometry_only: bool
child 3, without_dwell: bool
child 4, without_speed: bool
child 1, goal_success: bool
child 2, id: string
child 3, max_replay_pose_roundoff: double
child 4, mismatches: int64
child 5, replay_frames: int64
child 6, trace_samples: int64
counts: struct<native_checks_passed: int64, native_goal_successes: int64, native_scheduled: int64, native_va (... 204 chars omitted)
child 0, native_checks_passed: int64
child 1, native_goal_successes: int64
child 2, native_scheduled: int64
child 3, native_valid: int64
child 4, negative_goals_correctly_rejected: int64
child 5, negative_scheduled: int64
child 6, positive_goal_successes: int64
child 7, positive_scheduled: int64
child 8, prechecks_correctly_rejected: int64
child 9, prechecks_scheduled: int64
mismatches: int64
protocol_sha256: string
replay_frames: int64
schema: string
scope: string
trace_samples: int64
public_bytes: int64
learned_skill: bool
manifest: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
physical_robot_ready: bool
archive: struct<bytes: int64, files: int64, name: string, sha256: string>
child 0, bytes: int64
child 1, files: int64
child 2, name: string
child 3, sha256: string
audit: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
public_files: int64
to
{'archive': {'bytes': Value('int64'), 'files': Value('int64'), 'name': Value('string'), 'sha256': Value('string')}, 'audit': {'bytes': Value('int64'), 'sha256': Value('string')}, 'learned_skill': Value('bool'), 'manifest': {'bytes': Value('int64'), 'sha256': Value('string')}, 'physical_robot_ready': Value('bool'), 'public_bytes': Value('int64'), 'public_files': Value('int64'), 'schema': Value('string')}
because column names don't match
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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
auditor_sha256: string
cases: list<item: struct<ablations_ever_accept: struct<any_instance_only: bool, body_origin_only: bool, geo (... 190 chars omitted)
child 0, item: struct<ablations_ever_accept: struct<any_instance_only: bool, body_origin_only: bool, geometry_only: (... 178 chars omitted)
child 0, ablations_ever_accept: struct<any_instance_only: bool, body_origin_only: bool, geometry_only: bool, without_dwell: bool, wi (... 18 chars omitted)
child 0, any_instance_only: bool
child 1, body_origin_only: bool
child 2, geometry_only: bool
child 3, without_dwell: bool
child 4, without_speed: bool
child 1, goal_success: bool
child 2, id: string
child 3, max_replay_pose_roundoff: double
child 4, mismatches: int64
child 5, replay_frames: int64
child 6, trace_samples: int64
counts: struct<native_checks_passed: int64, native_goal_successes: int64, native_scheduled: int64, native_va (... 204 chars omitted)
child 0, native_checks_passed: int64
child 1, native_goal_successes: int64
child 2, native_scheduled: int64
child 3, native_valid: int64
child 4, negative_goals_correctly_rejected: int64
child 5, negative_scheduled: int64
child 6, positive_goal_successes: int64
child 7, positive_scheduled: int64
child 8, prechecks_correctly_rejected: int64
child 9, prechecks_scheduled: int64
mismatches: int64
protocol_sha256: string
replay_frames: int64
schema: string
scope: string
trace_samples: int64
public_bytes: int64
learned_skill: bool
manifest: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
physical_robot_ready: bool
archive: struct<bytes: int64, files: int64, name: string, sha256: string>
child 0, bytes: int64
child 1, files: int64
child 2, name: string
child 3, sha256: string
audit: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
public_files: int64
to
{'archive': {'bytes': Value('int64'), 'files': Value('int64'), 'name': Value('string'), 'sha256': Value('string')}, 'audit': {'bytes': Value('int64'), 'sha256': Value('string')}, 'learned_skill': Value('bool'), 'manifest': {'bytes': Value('int64'), 'sha256': Value('string')}, 'physical_robot_ready': Value('bool'), 'public_bytes': Value('int64'), 'public_files': Value('int64'), 'schema': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
DVIDIA task environments v0.2
Reusable local environment recipes for a pinned native PiPER arm, a five-collider open bin, and up to four original rigid object instances. A rigid shoe surrogate is compound box geometry; it is not a measured or deformable shoe. All dynamics parameters are authored assumptions.
The frozen passive benchmark retains 6 positive native scenes, 6 negative completion fixtures, and 4 invalid-layout prechecks. All 12 native outcomes matched their declarations; all 4 invalid layouts were rejected. A separate read-only audit reconstructed 13,262 recorded 1 ms state/goal samples and 1,337 saved replay frames with zero consistency mismatches. These counts measure scene and verifier checks, not a learned robot manipulation success rate.
Open the environment studio and recorded benchmark · Source and Terminal tools
Download task-environments-v02.zip, extract it, and serve the directory with python -m http.server 8000. The browser can edit a draft and replay saved native motion. Running new physics requires the local Python runtime and verified PiPER assets; a static hosted page does not itself run the native simulator. See guide.md for installation, local UI/Terminal commands, and supported inputs.
PiPER holds its native home controls. No grasp policy is trained, no video-to-skill learning or physical-robot transfer is demonstrated, and the consistency audit does not establish real-world physics or sensor accuracy. Negative controls explicitly disable gravity in two stationary verifier fixtures and set initial velocity with zero friction in one moving fixture; all interventions and attempts are retained. Geometry is stored once by content hash.
Original DVIDIA code and geometry: MIT; pinned PiPER simulation assets: MIT with notices in the archive. Other dependencies retain their own licenses. DVIDIA research · @iammrriver · hello@dvidia.org.
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