The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
Error code: DatasetGenerationError
Exception: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1531, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 127, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1405, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 982, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1393, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1571, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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... | mrfood1/data/chunk-000/episode_000000 | hf://datasets/angkul07/piper-single-arm-cotrain@33d26e9f44bccc6412339119130901a7b2924769/mrfood1.tar |
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0... | mrfood1/data/chunk-000/episode_000006 | hf://datasets/angkul07/piper-single-arm-cotrain@33d26e9f44bccc6412339119130901a7b2924769/mrfood1.tar |
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piper-single-arm-cotrain
Five LeRobot v2.1 datasets on an AgileX Piper arm, put on one schema so they can be mixed without a rename map: two built by retargeting human demonstrations, three from real teleoperation. Four form a duration-balanced training mixture; the fifth is a held-out validation set.
Training mixture
| archive | size | episodes | frames | duration | tasks | provenance |
|---|---|---|---|---|---|---|
piper_ego.tar |
172 MB | 1,574 | 109,730 | 1 h 31 m | 30 | EgoDex-derived egocentric pick-and-place, retargeted |
piper_stera_plate.tar |
75 MB | 226 | 20,385 | 17 m | 127 | stera-10m plate handling, retargeted |
mrfood1.tar |
1.62 GB | 294 | 61,709 | 51 m | 1 | real teleop |
mrfood3.tar |
1.74 GB | 285 | 68,271 | 57 m | 1 | real teleop |
Retargeted 130,115 frames vs teleop 129,980 — 1 h 48 m per side, 135 frames apart.
Validation
| archive | size | episodes | frames | duration | tasks | provenance |
|---|---|---|---|---|---|---|
mrfood2.tar |
1.85 GB | 347 | 67,991 | 57 m | 1 | real teleop, held out |
mrfood2 is a separate teleop recording session from the two in the
training mixture (2026-08-19, against 08-14/15 and 08-11), carrying the same
schema and the same transforms — but untrimmed, since a validation set
should not be truncated to a training budget.
Read what it measures narrowly: it is the same task (pick_and_lift_right) on
the same rig, so it scores cross-session generalisation within the teleop
domain. Its single task is exactly the one the teleop training half already
covers. It says nothing about the retargeted domain, which contributes 157 of
the mixture's 158 distinct task strings (the two halves share no task) and all
of its synthesized imagery. A model that
scores well here has not been shown to transfer to the retargeted distribution,
or to any unseen task.
Schema
Every archive — training and validation alike — is a self-contained LeRobot v2.1 dataset with an identical schema:
<dataset>/data/chunk-000/episode_NNNNNN.parquet
<dataset>/videos/chunk-000/observation.images.top/episode_NNNNNN.mp4
<dataset>/videos/chunk-000/observation.images.right-arm/episode_NNNNNN.mp4
<dataset>/meta/{info,episodes,tasks,episodes_stats}.jsonl + info.json
codebase_version |
v2.1 |
robot_type |
agilex_piper (single arm) |
| rate | 20 Hz / 20 fps |
observation.state, action |
float32[7] = [j1…j6 in degrees, gripper ∈ {0,1}] |
| cameras | observation.images.top, observation.images.right-arm |
Gripper is 0 = closed, 1 = open. Joint angles are degrees, within the
Piper limits (±150, 0–180, −155–0, ±105, ±70, ±180).
meta/source_episodes.jsonl maps each episode back to its index in the
pre-trim dataset; meta/source_clips.jsonl (retargeted) and
meta/episode_quality.jsonl (teleop) are carried through where the source had
them.
How these were unified
The sources did not agree, in ways that raise no error at load time:
- Left arm dropped. It was a constant park pose in the retargeted sets and exactly zero in the teleop sets — no signal either way. This also moves the gripper to index 6 everywhere; it had been at index 6 in the retargeted sets and index 12 in teleop.
- Radians → degrees on the retargeted sets. Teleop was already degrees.
- Gripper binarized. Teleop was already effectively binary (one transition per clip), so a plain 0.4 threshold applies. The retargeted gripper is a continuous hand-aperture signal that never reaches either stop; a plain threshold on it yields a median of 3–4 transitions per clip — phantom grasp/release events inside a single pick-and-place. It instead gets per-clip min/max normalisation and a Schmitt trigger (0.40 ±0.15, 8-frame dwell), giving a median of 2 transitions (grasp + release).
- 20 Hz. The retargeted sets were resampled from 30 Hz by linear interpolation onto a uniform 50 ms grid (30→20 is a 2/3 ratio, so decimation would leave alternating 33/67 ms gaps), with the action recomputed afterwards.
Gripper open-fraction after binarization: 0.64 / 0.49 (retargeted) against 0.56 / 0.51 (teleop).
Known limits
The retargeted views are synthetic, and share one camera. top and
right-arm are both crops of a single egocentric frame — top is the square
centre crop, right-arm a zoom window tracking a MediaPipe-detected grasp
point. They carry no parallax, unlike the teleop sets' two physical cameras.
Video is 224×224 upright for the retargeted sets and 480×640 portrait
(rotated ~90°) for teleop.
Action means slightly different things. The retargeted action is exactly
state[t+1] — a perfect next-frame target, because it is constructed that way.
The teleop action is a separately recorded controller command that leads the
state by one frame with a real tracking residual (~0.08°). Both are absolute
next-step position targets.
Joint envelopes only partly overlap. The retargeted trajectories reach every Piper joint limit; the teleop trajectories occupy a narrow interior band. About 73% of the retargeted saturation is wrist-driven (joint4/joint5; joint5's range is only ±1.22 rad, the tightest on the arm).
piper_stera_plate is the weakest set. Its retargeting FK error is
substantially higher than piper_ego's, and 10% of its clips still show more
than four gripper transitions after debouncing.
Grasp events are inferred, not labelled. The retargeted gripper signal comes from hand pose, not from a recorded gripper command.
Provenance and attribution
piper_ego,piper_stera_plate— retargeted with the Fidelity Dynamics dt-pipeline (stage 6) fromangkul07/piper-retargeted, itself derived from EgoDex-style egocentric recordings and stera-10m.mrfood1— fromPranayTest/pick-right-2026-08-14to15-2cammrfood3— fromPranayTest/pick-right-2026-08-11-2cam-prxxxmrfood2— fromPranayTest/pick-right-2026-08-19-2cam
The teleop sources are LeRobot v3.0 and were converted to v2.1 here (v3.0 packs
many episodes per parquet and concatenates episodes into shared video files;
splitting those requires a re-encode, since the cut points are not
keyframe-aligned). mrfood1 and mrfood3 were then trimmed by dropping whole
episodes in a golden-ratio order — so the cut spreads evenly across each run
rather than concentrating on one stretch of a session — to match the retargeted
duration. mrfood2 is untrimmed.
All upstream datasets are Apache-2.0.
Validation
Every episode in all five archives passes: parquet row count against declared
length, contiguous frame_index, timestamps against the declared rate,
globally continuous index, and a frame count for each declared camera key.
Zero errors.
Usage
tar xf piper_ego.tar
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("piper_ego", root="piper_ego")
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