xpeng-robotics/DIAL_checkpoints
Updated • 14 • 2
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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")
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Simulation GR1 Tabletop Task 1K Dataset
Dataset Description:
The PhysicalAI-Robotics-GR00T-Teleop-GR1 dataset consists of 1000 teleoperation trajectories in simulation using the GR1 robot with upper body control. The simulation setup mimics tabletop manipulation tasks and uses RGB observations with a virtual camera. The robot is equipped with simulated Fourier hands.
This dataset is ready for non-commercial use.
Dataset Owner(s):
NVIDIA GEAR
Dataset Creation Date:
June 1, 2025
License/Terms of Use:
This dataset is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Intended Usage:
Researchers, Academics, Open-Source Community: AI-driven robotics research and algorithm development in simulation environments. Developers: Integrate and customize AI policies for tabletop tasks. Startups & Companies: Non-commercial benchmarking and prototyping.
Dataset Characterization
Data Collection Method
[Human Teleoperation in Simulation]
Labeling Method
[Human]
Dataset Format
HDF5 format and LeRobot-style dataset
Modality:
- Observation: 44 dim of vectorized state (joint positions of upper body + waist)
- Action: 44 dim of vectorized action (joint positions of upper body + waist)
- Video: RGB video, 256x256 resolution (cropped and padded; original resolution is 51x320), 20 fps
- Language Instruction Example:
- "pick up the bottled water, place it into the cabinet and close the cabinet"
- "pick the cupcake from the cutting board and place it in the basket"
Dataset Quantification
- 1000 teleoperation trajectories for each of the 24 tabletop tasks
- Total data storage (HDF5 format): ~14GB
- Total data storage (LeRobot-style dataset): ~39GB
Download the dataset
huggingface-cli download \
--repo-type dataset nvidia/PhysicalAI-Robotics-GR00T-Teleop-Sim \
--local-dir ./datasets/
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