The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need 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-retargeted
Human demonstrations retargeted to an AgileX Piper arm with the Fidelity Dynamics dt-pipeline (stage 6). Two datasets, one repo.
| archive | size | clips | frames | source |
|---|---|---|---|---|
piper_ego_retargeted.tar |
2.2 GB | 1,574 | 164,601 | EgoDex-derived egocentric pick-and-place (ego_filtered, LeRobot v3.0, 30 tasks) |
piper_stera_plate_retargeted.tar |
332 MB | 226 | 30,584 | stera-10m plate handling (plate200_pp) |
Each archive is self-contained: the observation video sits in the same clip directory as the trajectory it belongs to, so there is nothing to join.
Layout
<dataset>/clips/<clip_id>/observation.mp4 # RGB observation for this clip
<dataset>/clips/<clip_id>/retargeted.hdf5 # trajectories + per-frame IK error
<dataset>/clips/<clip_id>/report.json # per-clip M1–M6 record, QA grades
<dataset>/manifest.json # aggregate stats + one row per clip
<dataset>/README.md
Video and trajectory are 1:1 per clip and share the clip's frame count and 30 fps
timebase. Archives are uncompressed .tar — the MP4s are already compressed, so
gzip would cost time and save nothing.
retargeted.hdf5:
| key | shape | meaning |
|---|---|---|
joint_positions |
(T, 14) | arms in R, L order — 6 joints + 1 gripper each |
ik_errors_R / _L |
(T,) | per-frame IK position error, metres |
base_R / _L |
(3,) | base placement chosen by BPP |
timestamps |
(T,) | seconds, 30 fps |
Attributes carry joint_names, active_arms, parked_arms, arm_mode,
n_arms, fps and the M4/QA records.
Robot
agilex_piper_bimanual. Stage 6 loads one single-arm MJCF and drives it once
per arm with its own base placement, so "bimanual" needs no two-arm model.
Joints joint1..joint6 plus jaw joint7 (joint8 is coupled = -joint7).
The TCP is the grasp_frame fingertip midpoint, not Link6.
Workspace placement used WORKSPACE_CENTER_MODE=capmap: the centre is the
reachability-index-weighted centroid of this arm's own pose-reachable voxels
([0.034, 0.000, 0.208] over 18,479 voxels), not a reference robot's vector
rescaled. Scale 0.8811 (reach ratio 1.037; Piper 0.897 m vs reference 0.865 m).
Quality
| ego | stera | |
|---|---|---|
| FK error (worst active arm) | mean 4.48 cm, median 3.87, p90 7.63 | mean 10.53 cm, median 7.20, p90 24.66 |
| saturation | mean 0.426 | mean 0.506 |
| QA PASS / WARN / FAIL | 262 / 1012 / 300 | 19 / 111 / 96 |
| dominant FAIL cause | saturation (331) | saturation (89), self-collision (18) |
Filter, don't discard. qa_verdict gates on the worst single arm joint's
fraction of frames pinned at a limit, so one saturated DOF fails a clip whose
tracking is otherwise good. manifest.json carries per-clip ik_mean_cm and
saturation if you would rather threshold yourself. The stera set is
substantially the weaker of the two.
Per-dataset notes
ego — both arms are active on every clip, but not equally busy: the right arm travels a median 1.00 m per clip against the left's 0.025 m and carries the error (4.25 vs 0.44 cm). A near-static arm can still saturate by resting against a limit; 139 of the 300 FAILs are left-arm.
stera — single-arm (--arm right); the left arm is a constant park pose
carrying no signal, so drop columns 7–13 for single-arm training. 29 of the
255 selected clips are absent because the human demonstrator used their left
hand, dropping the right below the 50% tracking threshold; they are listed in
manifest.json under skipped_clips. Because M3.5 re-solves the moving arm to
avoid the parked one, 18 clips FAIL on selfcol for a collision that will not
exist once the left arm is dropped.
Known limit
About 73% of Piper's joint saturation is wrist-driven (joint4 and joint5; joint5's range is only ±1.22 rad, the tightest on the arm). A workspace re-placement cannot fix an orientation the wrist cannot reach, which is why the teleop-supervised refit is disabled for these runs — measured, it accepted 2 of 150 clips. The remaining lever is orientation-side, not placement.
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