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: CastError
Message: Couldn't cast
schema: string
scope: string
sample_timing: string
native_timestep_s: double
rows: int64
fields: list<item: struct<name: string, width: int64>>
child 0, item: struct<name: string, width: int64>
child 0, name: string
child 1, width: int64
side_order: list<item: string>
child 0, item: string
joint_order: list<item: string>
child 0, item: string
jaw_order: list<item: string>
child 0, item: string
pulses: list<item: null>
child 0, item: null
pulse_tick_schedule: list<item: null>
child 0, item: null
pulse_actuation: string
jaw_target_rate_m_s: double
contact_names: struct<geoms: list<item: struct<geom_id: int64, geom_name: string, body_id: int64, body_name: string (... 142 chars omitted)
child 0, geoms: list<item: struct<geom_id: int64, geom_name: string, body_id: int64, body_name: string>>
child 0, item: struct<geom_id: int64, geom_name: string, body_id: int64, body_name: string>
child 0, geom_id: int64
child 1, geom_name: string
child 2, body_id: int64
child 3, body_name: string
child 1, pad_geom_ids: struct<left: list<item: int64>, right: list<item: int64>>
child 0, left: list<item: int64>
child 0, item: int64
child 1, right: list<item: int64>
child 0, item: int64
child 2, block_geom_id: int64
child 3, table_geom_id: int64
child 4, floor_geom_id: int64
unchanged: list<item: string>
child 0, item: string
recovery_policy: null
physical_robot_ready: bool
post_grip_inte
...
child 12, in_grasp_window: bool
child 13, acquisition_released: bool
child 14, gate_progress_s: double
child 15, release_progress_s: double
case_id: string
condition: string
jaw_mode: string
base_model_sha256: string
native_servo_gains: list<item: double>
child 0, item: double
metrics: struct<valid: bool, success: bool, reason: string, simulation_time: double, steps: int64, lift_seen: (... 561 chars omitted)
child 0, valid: bool
child 1, success: bool
child 2, reason: string
child 3, simulation_time: double
child 4, steps: int64
child 5, lift_seen: bool
child 6, grasp_seen: bool
child 7, max_object_lift_m: double
child 8, dwell_seconds: double
child 9, lift_dwell_seconds: double
child 10, max_pad_normal_force_n: double
child 11, unexpected_contact_native_ticks: int64
child 12, carry_complete: bool
child 13, retention_lost: bool
child 14, max_carry_contact_loss_seconds: double
child 15, carry_contact_loss_events: int64
child 16, carry_contact_loss_tolerance_seconds: double
child 17, first_success_time_seconds: double
child 18, target_distance_m: double
child 19, warnings: list<item: null>
child 0, item: null
child 20, physical_robot_ready: bool
child 21, contact_pair_native_ticks: struct<1:90: int64, 88:90: int64, 89:90: int64, 86:90: int64, 85:90: int64>
child 0, 1:90: int64
child 1, 88:90: int64
child 2, 89:90: int64
child 3, 86:90: int64
child 4, 85:90: int64
to
{'schema': Value('string'), 'config': {'timestep': Value('float64'), 'control_dt': Value('float64'), 'timeout_s': Value('float64'), 'settle_s': Value('float64'), 'object_position_m': List(Value('float64')), 'target_position_m': List(Value('float64')), 'object_half_size_m': List(Value('float64')), 'mass_kg': Value('float64'), 'friction': Value('float64'), 'table_top_m': Value('float64'), 'table_center_xy_m': List(Value('float64')), 'table_half_size_xy_m': List(Value('float64')), 'table_thickness_m': Value('float64'), 'goal_position_tolerance_m': Value('float64'), 'goal_dwell_s': Value('float64'), 'min_lift_m': Value('float64'), 'min_lift_dwell_s': Value('float64'), 'retreat_clearance_m': Value('float64'), 'joint_target_rate_rad_s': Value('float64'), 'jaw_target_rate_m_s': Value('float64'), 'tool_orientation_wxyz': List(Value('float64')), 'carry_orientation_wxyz': List(Value('float64'))}, 'case_id': Value('string'), 'seed': Value('int64'), 'condition': Value('string'), 'policy_kind': Value('string'), 'base_model_sha256': Value('string'), 'jaw_model_sha256': Value('string'), 'model_sha256': Value('null'), 'jaw_mode': Value('string'), 'teacher_config': {'gate_offset_s': Value('float64'), 'minimum_pad_force_n': Value('float64'), 'secure_dwell_s': Value('float64'), 'maximum_relative_drift_m': Value('float64'), 'acquisition_only': Value('bool')}, 'post_grip_parameters': {'force_threshold_n': Value('float64'), 'lift_threshold_m': Value('float64'), 'eligibility_dwell_s': Value('float6
...
d_model_sha256': Value('string'), 'asset_provenance': {'profile': Value('string'), 'repository': Value('string'), 'commit': Value('string'), 'files_verified': Value('int64'), 'bytes_verified': Value('int64'), 'lock_sha256': Value('string'), 'simulation_asset_license': Value('string')}, 'native_servo_gains': List(Value('float64')), 'native_force_limits': List(List(Value('float64'))), 'platform': {'system': Value('string'), 'machine': Value('string'), 'python': Value('string'), 'numpy': Value('string'), 'mujoco': Value('string')}, 'policy_diagnostics': {'schema': Value('string'), 'progress_seconds': Value('float64'), 'physical_action_calls': Value('int64'), 'inference_seconds': Value('float64'), 'original_arm_path_preserved': Value('bool'), 'jaw_mode': Value('string'), 'frozen_jaw_clock': Value('string'), 'frozen_jaw_model_sha256': Value('string'), 'base_model_sha256': Value('string'), 'physical_robot_ready': Value('bool')}, 'timing': {'wall_seconds': Value('float64'), 'inference': {'samples': Value('int64'), 'p50_s': Value('float64'), 'p95_s': Value('float64'), 'p99_s': Value('float64'), 'maximum_s': Value('float64')}, 'control_step': {'samples': Value('int64'), 'p50_s': Value('float64'), 'p95_s': Value('float64'), 'p99_s': Value('float64'), 'maximum_s': Value('float64')}, 'inference_samples_s': List(Value('float64')), 'control_step_samples_s': List(Value('float64')), 'missed_20ms_boundaries': Value('int64'), 'process_peak_rss_bytes': Value('int64'), 'scope': 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
schema: string
scope: string
sample_timing: string
native_timestep_s: double
rows: int64
fields: list<item: struct<name: string, width: int64>>
child 0, item: struct<name: string, width: int64>
child 0, name: string
child 1, width: int64
side_order: list<item: string>
child 0, item: string
joint_order: list<item: string>
child 0, item: string
jaw_order: list<item: string>
child 0, item: string
pulses: list<item: null>
child 0, item: null
pulse_tick_schedule: list<item: null>
child 0, item: null
pulse_actuation: string
jaw_target_rate_m_s: double
contact_names: struct<geoms: list<item: struct<geom_id: int64, geom_name: string, body_id: int64, body_name: string (... 142 chars omitted)
child 0, geoms: list<item: struct<geom_id: int64, geom_name: string, body_id: int64, body_name: string>>
child 0, item: struct<geom_id: int64, geom_name: string, body_id: int64, body_name: string>
child 0, geom_id: int64
child 1, geom_name: string
child 2, body_id: int64
child 3, body_name: string
child 1, pad_geom_ids: struct<left: list<item: int64>, right: list<item: int64>>
child 0, left: list<item: int64>
child 0, item: int64
child 1, right: list<item: int64>
child 0, item: int64
child 2, block_geom_id: int64
child 3, table_geom_id: int64
child 4, floor_geom_id: int64
unchanged: list<item: string>
child 0, item: string
recovery_policy: null
physical_robot_ready: bool
post_grip_inte
...
child 12, in_grasp_window: bool
child 13, acquisition_released: bool
child 14, gate_progress_s: double
child 15, release_progress_s: double
case_id: string
condition: string
jaw_mode: string
base_model_sha256: string
native_servo_gains: list<item: double>
child 0, item: double
metrics: struct<valid: bool, success: bool, reason: string, simulation_time: double, steps: int64, lift_seen: (... 561 chars omitted)
child 0, valid: bool
child 1, success: bool
child 2, reason: string
child 3, simulation_time: double
child 4, steps: int64
child 5, lift_seen: bool
child 6, grasp_seen: bool
child 7, max_object_lift_m: double
child 8, dwell_seconds: double
child 9, lift_dwell_seconds: double
child 10, max_pad_normal_force_n: double
child 11, unexpected_contact_native_ticks: int64
child 12, carry_complete: bool
child 13, retention_lost: bool
child 14, max_carry_contact_loss_seconds: double
child 15, carry_contact_loss_events: int64
child 16, carry_contact_loss_tolerance_seconds: double
child 17, first_success_time_seconds: double
child 18, target_distance_m: double
child 19, warnings: list<item: null>
child 0, item: null
child 20, physical_robot_ready: bool
child 21, contact_pair_native_ticks: struct<1:90: int64, 88:90: int64, 89:90: int64, 86:90: int64, 85:90: int64>
child 0, 1:90: int64
child 1, 88:90: int64
child 2, 89:90: int64
child 3, 86:90: int64
child 4, 85:90: int64
to
{'schema': Value('string'), 'config': {'timestep': Value('float64'), 'control_dt': Value('float64'), 'timeout_s': Value('float64'), 'settle_s': Value('float64'), 'object_position_m': List(Value('float64')), 'target_position_m': List(Value('float64')), 'object_half_size_m': List(Value('float64')), 'mass_kg': Value('float64'), 'friction': Value('float64'), 'table_top_m': Value('float64'), 'table_center_xy_m': List(Value('float64')), 'table_half_size_xy_m': List(Value('float64')), 'table_thickness_m': Value('float64'), 'goal_position_tolerance_m': Value('float64'), 'goal_dwell_s': Value('float64'), 'min_lift_m': Value('float64'), 'min_lift_dwell_s': Value('float64'), 'retreat_clearance_m': Value('float64'), 'joint_target_rate_rad_s': Value('float64'), 'jaw_target_rate_m_s': Value('float64'), 'tool_orientation_wxyz': List(Value('float64')), 'carry_orientation_wxyz': List(Value('float64'))}, 'case_id': Value('string'), 'seed': Value('int64'), 'condition': Value('string'), 'policy_kind': Value('string'), 'base_model_sha256': Value('string'), 'jaw_model_sha256': Value('string'), 'model_sha256': Value('null'), 'jaw_mode': Value('string'), 'teacher_config': {'gate_offset_s': Value('float64'), 'minimum_pad_force_n': Value('float64'), 'secure_dwell_s': Value('float64'), 'maximum_relative_drift_m': Value('float64'), 'acquisition_only': Value('bool')}, 'post_grip_parameters': {'force_threshold_n': Value('float64'), 'lift_threshold_m': Value('float64'), 'eligibility_dwell_s': Value('float6
...
d_model_sha256': Value('string'), 'asset_provenance': {'profile': Value('string'), 'repository': Value('string'), 'commit': Value('string'), 'files_verified': Value('int64'), 'bytes_verified': Value('int64'), 'lock_sha256': Value('string'), 'simulation_asset_license': Value('string')}, 'native_servo_gains': List(Value('float64')), 'native_force_limits': List(List(Value('float64'))), 'platform': {'system': Value('string'), 'machine': Value('string'), 'python': Value('string'), 'numpy': Value('string'), 'mujoco': Value('string')}, 'policy_diagnostics': {'schema': Value('string'), 'progress_seconds': Value('float64'), 'physical_action_calls': Value('int64'), 'inference_seconds': Value('float64'), 'original_arm_path_preserved': Value('bool'), 'jaw_mode': Value('string'), 'frozen_jaw_clock': Value('string'), 'frozen_jaw_model_sha256': Value('string'), 'base_model_sha256': Value('string'), 'physical_robot_ready': Value('bool')}, 'timing': {'wall_seconds': Value('float64'), 'inference': {'samples': Value('int64'), 'p50_s': Value('float64'), 'p95_s': Value('float64'), 'p99_s': Value('float64'), 'maximum_s': Value('float64')}, 'control_step': {'samples': Value('int64'), 'p50_s': Value('float64'), 'p95_s': Value('float64'), 'p99_s': Value('float64'), 'maximum_s': Value('float64')}, 'inference_samples_s': List(Value('float64')), 'control_step_samples_s': List(Value('float64')), 'missed_20ms_boundaries': Value('int64'), 'process_peak_rss_bytes': Value('int64'), 'scope': 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.
PiPER grip clock v0.3 synthetic study dataset
Published as Dvidia/piper-grip-clock-demonstrations-v03, this dataset preserves the finite lineage for the simulation-only PiPER grip-clock v0.3 experiment: 64 synthetic acquisition-teacher demonstrations, four finite candidate fits, 192 development attempts, and 512 fresh confirmation attempts. All 768 scheduled attempts were independently reconstructed with zero numeric audit mismatches.
The confirmation comparison uses 32 fresh XY groups. Each group contains two known destinations and nominal/post-grip conditions, paired across the full clock, matched blind clock, fixed reference, and acquisition teacher. The saved report retains every scheduled attempt in each denominator.
| Confirmation controller | Strict successes | Valid attempts |
|---|---|---|
| clock | 44/128 | 128/128 |
| blind_clock | 26/128 | 128/128 |
| fixed | 72/128 | 128/128 |
| teacher | 67/128 | 128/128 |
Full minus blind_clock: +14.1 percentage points (approximate 97.5% interval -8.5 to +36.6). Full minus fixed: -21.9 percentage points (approximate 97.5% interval -36.8 to -7.0). These are paired XY-group t intervals with Bonferroni simultaneous 95% coverage under their assumptions. They are exploratory, not a powered qualification. All predeclared follow-up gates passed: false. Hardware qualification remains false.
Data scope
Episodes are authored simulator outputs for one synthetic 40 g rigid block, two known destinations, and a narrow PiPER start envelope. Training labels come from a causal sampled acquisition teacher using privileged simulator state. The teacher checks bilateral contact/force and tool-relative drift across 100 ms, then releases a latch; this dataset does not demonstrate continuous learned recovery. It contains no human videos, camera observations, physical robot trials, arbitrary-object transfer, or hardware qualification.
The study/ directory is a byte-preserving copy of the frozen study, excluding only its local lifecycle lock. audit.json contains the independent all-partition numeric audit. docs/ includes the reproduction guide, measured shared-host record, and development lineage. integrity.json binds every other file in this payload by size and SHA-256.
DVIDIA-authored code and synthetic numeric evidence are MIT licensed. No robot asset bundle is included. Upstream asset notices remain applicable when assets are fetched separately. Questions: hello@dvidia.org · X: @iammrriver.
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