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: struct<start_s: double, end_s: double, minimum_opening_m: double>>
child 0, item: struct<start_s: double, end_s: double, minimum_opening_m: double>
child 0, start_s: double
child 1, end_s: double
child 2, minimum_opening_m: double
pulse_tick_schedule: list<item: struct<start_tick: int64, end_tick: int64, minimum_opening_m: double>>
child 0, item: struct<start_tick: int64, end_tick: int64, minimum_opening_m: double>
child 0, start_tick: int64
child 1, end_tick: int64
child 2, minimum_opening_m: double
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
...
geom_ids: list<item: int64>, normal_force_n: double>
child 0, geom_ids: list<item: int64>
child 0, item: int64
child 1, normal_force_n: double
child 19, native_actuator_controls: list<item: double>
child 0, item: double
child 20, actuator_force_native: list<item: double>
child 0, item: double
child 21, issued_joint_targets_rad: list<item: double>
child 0, item: double
child 22, issued_gripper_position_m: double
child 23, command: struct<t_seconds: double, joint_targets_rad: list<item: double>, gripper_position_m: double, expert_ (... 127 chars omitted)
child 0, t_seconds: double
child 1, joint_targets_rad: list<item: double>
child 0, item: double
child 2, gripper_position_m: double
child 3, expert_action: struct<joint_targets_rad: list<item: double>, gripper_position_m: double>
child 0, joint_targets_rad: list<item: double>
child 0, item: double
child 1, gripper_position_m: double
child 4, executed_expert: bool
child 5, expert_phase: string
child 24, observation_origin: string
seed: int64
valid: bool
base_model_sha256: string
platform: struct<system: string, machine: string, python: string, numpy: string, mujoco: string>
child 0, system: string
child 1, machine: string
child 2, python: string
child 3, numpy: string
child 4, mujoco: string
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'), 'pulses': List({'start_s': Value('float64'), 'end_s': Value('float64'), 'minimum_opening_m': Value('float64')}), 'policy_kind': Value('string'), 'base_model_sha256': Value('string'), 'model_sha256': Value('string'), 'physical_robot_ready': Value('bool'), 'observation_origin': Value('string'), 'trajectory': List({'t_seconds': Value('float64'), 'qpos': List(Value('float64')), 'qvel': List(Value('float64')), 'joint_position_rad': List(Value('float64')), 'joint_velocity_rad_s': List(Value('float64')), 'gripper_joint_
...
ue('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': {'policy_origin': Value('string'), 'blind': Value('bool'), 'student_inference_calls': Value('int64'), 'student_inference_seconds': Value('float64'), 'residual_clipped_components': Value('int64'), 'teacher_fallback_calls': Value('int64'), 'runtime_teacher_available': Value('bool'), 'authored_task_phase_machine': Value('bool'), 'runtime_ik_available': Value('bool'), 'reactive_contact_feedback': Value('bool'), 'maximum_history_seconds': Value('float64'), 'physical_robot_ready': Value('bool'), 'base_model_sha256': Value('string'), 'feedback_model_sha256': Value('string')}, '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: struct<start_s: double, end_s: double, minimum_opening_m: double>>
child 0, item: struct<start_s: double, end_s: double, minimum_opening_m: double>
child 0, start_s: double
child 1, end_s: double
child 2, minimum_opening_m: double
pulse_tick_schedule: list<item: struct<start_tick: int64, end_tick: int64, minimum_opening_m: double>>
child 0, item: struct<start_tick: int64, end_tick: int64, minimum_opening_m: double>
child 0, start_tick: int64
child 1, end_tick: int64
child 2, minimum_opening_m: double
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
...
geom_ids: list<item: int64>, normal_force_n: double>
child 0, geom_ids: list<item: int64>
child 0, item: int64
child 1, normal_force_n: double
child 19, native_actuator_controls: list<item: double>
child 0, item: double
child 20, actuator_force_native: list<item: double>
child 0, item: double
child 21, issued_joint_targets_rad: list<item: double>
child 0, item: double
child 22, issued_gripper_position_m: double
child 23, command: struct<t_seconds: double, joint_targets_rad: list<item: double>, gripper_position_m: double, expert_ (... 127 chars omitted)
child 0, t_seconds: double
child 1, joint_targets_rad: list<item: double>
child 0, item: double
child 2, gripper_position_m: double
child 3, expert_action: struct<joint_targets_rad: list<item: double>, gripper_position_m: double>
child 0, joint_targets_rad: list<item: double>
child 0, item: double
child 1, gripper_position_m: double
child 4, executed_expert: bool
child 5, expert_phase: string
child 24, observation_origin: string
seed: int64
valid: bool
base_model_sha256: string
platform: struct<system: string, machine: string, python: string, numpy: string, mujoco: string>
child 0, system: string
child 1, machine: string
child 2, python: string
child 3, numpy: string
child 4, mujoco: string
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'), 'pulses': List({'start_s': Value('float64'), 'end_s': Value('float64'), 'minimum_opening_m': Value('float64')}), 'policy_kind': Value('string'), 'base_model_sha256': Value('string'), 'model_sha256': Value('string'), 'physical_robot_ready': Value('bool'), 'observation_origin': Value('string'), 'trajectory': List({'t_seconds': Value('float64'), 'qpos': List(Value('float64')), 'qvel': List(Value('float64')), 'joint_position_rad': List(Value('float64')), 'joint_velocity_rad_s': List(Value('float64')), 'gripper_joint_
...
ue('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': {'policy_origin': Value('string'), 'blind': Value('bool'), 'student_inference_calls': Value('int64'), 'student_inference_seconds': Value('float64'), 'residual_clipped_components': Value('int64'), 'teacher_fallback_calls': Value('int64'), 'runtime_teacher_available': Value('bool'), 'authored_task_phase_machine': Value('bool'), 'runtime_ik_available': Value('bool'), 'reactive_contact_feedback': Value('bool'), 'maximum_history_seconds': Value('float64'), 'physical_robot_ready': Value('bool'), 'base_model_sha256': Value('string'), 'feedback_model_sha256': Value('string')}, '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.
DVIDIA · PiPER learned feedback v0.2
A simulation research pilot: a learned controller paired with the frozen v0.1 motion primitive, trained on synthetic authored-expert labels and privileged simulator sensing. The matched blind control is separately trained without live feedback. Native servos, actuator limits and gravity compensation remain authored. This is not human-video learning, general slip recovery, hardware qualification, or proof of one-shot robot skill transfer.
| Variant | Strict successes / scheduled | Valid |
|---|---|---|
| feedback | 6 / 24 | 24 |
| blind | 8 / 24 | 24 |
| feedforward | 5 / 24 | 24 |
| teacher | 24 / 24 | 24 |
| open_jaw | 0 / 24 | 24 |
All scheduled outcomes are retained, including failures. The dataset contains original 1 ms native NPZ arrays, teacher demonstrations, every development candidate, the DAgger collection (if used), confirmation traces, sources and receipts. The static replay is a smaller recorded projection; it does not run physics or inference in the browser.
When training-parent.json is present, original teachers and failed experiments live unchanged under prior-studies/study01/ with their original protocol and source hashes. Preserving the old DAgger collection does not imply that the selected new model trains on those mixed-policy samples; consult its bound training receipt for the actual recipe.
Model · Complete evidence dataset · Recorded replay · Research paper · Source and local CLI
Limits
- Synthetic authored-expert jaw labels and privileged simulator sensing; not human video or hardware.
- Only the jaw is controlled by the new learned adapter; all six arm commands remain the frozen v0.1 primitive.
- Same rigid 40 g block, two trained destinations and narrow starting region as v0.1.
- Development starts were reused after five full-residual candidates each failed all 16 development cases.
- Interventions affect acquisition; airborne regrasp and general slip recovery are not established.
- Native servos, joint/jaw caps and gravity compensation remain authored.
- Timing excludes sensing, network transport and physical actuators; no hard real-time guarantee.
Original DVIDIA code and artifacts: MIT License. Upstream robot assets and rendering components retain their licenses; notices are included where applicable. Hashes establish artifact consistency, not independent authenticity.
Scientific README preservation
This Hub card is a publication metadata overlay. The exact original root scientific README is preserved byte for byte. The original scientific integrity manifest remains unchanged; apply the explicit README path mapping when verifying the original scientific file inventory.
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