Libero Plus in LeRobot V3
Collection
4 items • Updated
Dataset Viewer
The dataset viewer is not available for this dataset.
The JWT signature verification failed. Check the signing key and the algorithm.
Error code: JWTInvalidSignature
Exception: InvalidSignatureError
Message: Signature verification failed
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
decoded = jwt.decode(
jwt=token,
...<2 lines>...
options=options,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
decoded = self.decode_complete(
jwt,
...<8 lines>...
leeway=leeway,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
decoded = self._jws.decode_complete(
jwt,
...<3 lines>...
detached_payload=detached_payload,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
self._verify_signature(
~~~~~~~~~~~~~~~~~~~~~~^
signing_input,
^^^^^^^^^^^^^^
...<4 lines>...
options=merged_options,
^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
raise InvalidSignatureError("Signature verification failed")
jwt.exceptions.InvalidSignatureError: Signature verification failedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
libero_plus_object: detailed LeRobot v3.0
This dataset was converted from the LIBERO Plus LeRobot v2.1 libero_plus_object partition.
The original 8D state and 7D action vectors are preserved exactly as
raw_state.ref_state and raw_action.ref_action. Canonical low-dimensional fields follow
failure_rollout_data/dataset.md; debug.gripper_eef_* contains the ground-truth next-step
relative EEF motion for inspection.
Required camera transform for canonical training
The source observation.images.front and observation.images.wrist videos are preserved
unchanged. For canonical training, horizontally flip both camera views at load time. The
videos in this repository are deliberately not rewritten or re-encoded.
The source-pipeline root cause is a composition of two image transforms: robosuite returns a vertically inverted render, and the original dataset writer then rotates it by 180 degrees (flips both image axes). The vertical flips cancel, leaving the stored image horizontally mirrored. This is also tracked in LeRobot issue #3830.
For an array whose layout ends in (height, width, channels):
image = np.flip(image, axis=-2)
For a tensor whose width is the last dimension, such as (..., channels, height, width):
image = torch.flip(image, dims=(-1,))
Apply the image transform only to the camera pixels. Do not flip or negate raw_state.*,
raw_target.*, state.*, target.*, or debug.*; those fields remain proper right-handed
coordinate representations.
Conversion notes
- No frames were filtered. The historical LIBERO no-op predicate is audit-only; see
meta/noop_audit.jsonandmeta/noop_audit_episodes.jsonl. - Rotation and reconstruction checks are recorded in
meta/conversion_validation.json. - Alignment and controller-scale assumptions are recorded in
meta/conversion_config.json. meta/stats.jsonincludesq01andq99for every numeric and video feature. Numeric quantiles use every frame; video quantiles use a deterministic uniform sample of stored frames.
- Downloads last month
- 20