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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.
Egocentric Dataset for Physical AI and Robotics
The dataset contains 4,050 hours of first-person videos for egocentric vision and egocentric tracking. Featuring multimodal data from egocentric views, it includes data annotations and motion capture for extracting 3d poses. It provides detailed 3d objects and 3d scenes using visual data from VR headsets to analyze hands motions and pose estimations. .- Get the data
Dataset characteristics:
| Characteristic | Data |
|---|---|
| Description | Egocentric video recordings of daily activities in home environments |
| Data types | Video |
| Tasks | Hand Activity Recognition Egocentric Action Recognition Hand-Object Interaction |
| Hours of recordings | 4,050 |
| Hardware setups | 2 |
| Setup 1 (Pico + Motion Trackers) | 2,321 hours (57.3%) — natural speed, slow-motion, and real-speed object transferring |
| Setup 2 (Zed + Pico + Motion Trackers) | 1,729 hours (42.7%) — scripted object transfer tasks with spatial depth + egocentric view |
| Scenarios | 13 (sorting unsorted items, arranging products by category, collecting items into a container, transferring from drawer to table, wardrobe & table & bag, transport box & display table, folding fabric items, lids & cookware & drawers, transferring with a spoon, transferring with tongs, packing into containers, two-handed sorting, assembly & disassembly) |
| Environments | Kitchen, bathroom, living room, and other home settings |
| Activities | Sorting, transferring, folding, assembly/disassembly, tool use, two-handed manipulation |
📊 Sample dataset available! For full access, contact us to discuss purchase terms.
Dataset structure
- Setup 1 (Pico + Motion Trackers): 2,321 hours (57.3%) — natural speed, slow-motion, and real-speed object transferring, with hands appearing as needed or always in frame for detailed kinematics.
- Setup 2 (Zed + Pico + Motion Trackers): 1,729 hours (42.7%) — scripted object transfer tasks combining spatial depth from stereo Zed cameras with egocentric view from Pico headset.
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