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
embodiment: string
dimensionally_compatible_with_gr1_humanoid: bool
channels: list<item: string>
child 0, item: string
features: struct<observation.state: struct<dtype: string, shape: list<item: int64>>, action: struct<dtype: str (... 91 chars omitted)
child 0, observation.state: struct<dtype: string, shape: list<item: int64>>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 1, action: struct<dtype: string, shape: list<item: int64>>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, timestamp: struct<dtype: string, shape: list<item: int64>>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
num_episodes: int64
num_frames: int64
profiles_covered: list<item: string>
child 0, item: string
gap_behaviors_covered: list<item: string>
child 0, item: string
seeded_from_real_robot_pose: bool
wired_into_unified_trainer: bool
why_not_wired: string
agreement: null
pinned: bool
started_unix: double
duration_s: double
frames: int64
pilot: string
task: string
to
{'pilot': Value('string'), 'task': Value('string'), 'channels': List(Value('string')), 'started_unix': Value('float64'), 'pinned': Value('bool'), 'frames': Value('int64'), 'duration_s': Value('float64'), 'agreement': Value('null')}
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 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 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
embodiment: string
dimensionally_compatible_with_gr1_humanoid: bool
channels: list<item: string>
child 0, item: string
features: struct<observation.state: struct<dtype: string, shape: list<item: int64>>, action: struct<dtype: str (... 91 chars omitted)
child 0, observation.state: struct<dtype: string, shape: list<item: int64>>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 1, action: struct<dtype: string, shape: list<item: int64>>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, timestamp: struct<dtype: string, shape: list<item: int64>>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
num_episodes: int64
num_frames: int64
profiles_covered: list<item: string>
child 0, item: string
gap_behaviors_covered: list<item: string>
child 0, item: string
seeded_from_real_robot_pose: bool
wired_into_unified_trainer: bool
why_not_wired: string
agreement: null
pinned: bool
started_unix: double
duration_s: double
frames: int64
pilot: string
task: string
to
{'pilot': Value('string'), 'task': Value('string'), 'channels': List(Value('string')), 'started_unix': Value('float64'), 'pinned': Value('bool'), 'frames': Value('int64'), 'duration_s': Value('float64'), 'agreement': Value('null')}
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.
AiNex Gap-Fill Kinematics v1
LeRobot-schema motion dataset for the Hiwonder AiNex 24-DOF humanoid, covering the motion behaviors a bare walking-gait generator leaves out: gait initiation/termination, idle-standing "life" (breathing, weight wander, head glances), gaze control, in-place yaw steering, and shove-recovery reflexes. Generated 2026-07-21.
What this is
5 episodes (one per "swagger" style — default, strut, sneak, march,
tired), 2,350 frames total (47.0s @ 50Hz), 16 joint channels per frame.
Each episode scripts the full behavior repertoire in sequence:
idle (2.0s) -> gait ramp-in (1.8s) -> steer (1.0s) -> gaze look (1.0s)
-> reflex shove + decay (0.8s) -> gait ramp-out (1.8s) -> idle settle (1.0s)
action is the commanded channel vector from a hierarchical whole-body
controller: an operational-space Jacobian balance task (CoM position +
stance-foot flatness, built from AiNex's real URDF leg geometry) as the
primary task, with the gait/swagger generator's joint targets projected
into its null-space as the secondary task — so style can influence
the walk but can never override balance, by construction. observation.state
is that same trajectory offset by a real servo read-back taken from the
physical robot immediately before recording (see seed_pose_and_manifest.json
— all 16 servos, standing pose, read via
ros_robot_controller/bus_servo/get_position), so frame 0 of every episode
matches the physical robot exactly.
What this is NOT (read before using)
- Not torque control. AiNex's HiWonder bus servos are position-command
only (no torque input, no torque sensing). The controller is the
kinematic form of hierarchical WBC — position/velocity targets through
a damped-pseudoinverse Jacobian + null-space projector — not the torque
form
tau = J^T F + N tau_cpgsometimes quoted for this architecture. - Not directly compatible with GR1-humanoid GR00T training. NVIDIA's
Isaac-GR00T pipeline (
unified_trainer.py's GR00T stage in this project) trains against a fixed 44-dim GR1-humanoidEmbodimentTag/modality_config. This dataset is a 16-dim AiNex-specific schema — a real dimensional mismatch, not a formatting issue. Using it for GR00T fine-tuning requires adding a real AiNex embodiment definition to Isaac-GR00T first; that has not been done yet. - Not motion-captured from a walking robot. The robot was standing
still during recording (physically safe, pre-bench-walk).
actionis the controller's own commanded trajectory, which is the real quantity a policy imitating this controller would need to learn — but it is not an external mocap/vision measurement of the robot in motion.
Files
episodes/ep_*.npz—observation.state(T,16),action(T,16),timestamp(T,) float32 arrays, one per episodeepisodes/ep_*.json— episode metadata (pilot, task, channels, frames, duration, agreement)meta_info.json— schema, channel names, embodiment note, honest wiring-status flagsmeta_stats.json— real computed mean/std/min/max/q01/q99 over all 2,350 frames forobservation.stateandactionseed_pose_and_manifest.json— the real robot pose used to seed every episode + per-profile episode IDs/frame counts
Channels (16, in array order)
l_hip_roll, l_hip_pitch, l_knee, l_ankle_pitch, l_ankle_roll,
r_hip_roll, r_hip_pitch, r_knee, r_ankle_pitch, r_ankle_roll,
l_shoulder, r_shoulder, l_hip_yaw, r_hip_yaw, head_pan, head_tilt
(all radians; leg channels pass a balance filter + joint-limit avoidance; downstream hardware adapter applies pulse clamping + a 12-pulse/tick velocity limit not reflected in these values)
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