Datasets:

tanhuajie2001
/

Robo-Dopamine-Bench

Name: Robo-Dopamine-Bench
Creator: tanhuajie2001
License: https://choosealicense.com/licenses/apache-2.0/

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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 failed

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Benchmark for "Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation"

Joy is dopamine’s handiwork—whether in humans or in robotics.

Please follow the steps below to use this benchmark

🛠️ Setup

# clone repo.
git clone https://github.com/FlagOpen/Robo-Dopamine.git
cd Robo-Dopamine

# build conda env.
conda create -n robo-dopamine python=3.10
conda activate robo-dopamine
pip install -r requirements.txt

🔍 Evaluation

0. Download `Robo-Dopamine-Bench` from huggingface.

# download benchmark
huggingface-cli download --repo-type dataset --resume-download tanhuajie2001/Robo-Dopamine-Bench --local-dir ./Robo-Dopamine-Bench

# unzip images
cd Robo-Dopamine-Bench
unzip image.zip
cd ..

1. Evaluate local GRM with vLLM.

export CUDA_VISIBLE_DEVICES=0 
python -m eval.evaluation_grm \
  --model_path tanhuajie2001/Robo-Dopamine-GRM-3B \
  --input_json_dir ./Robo-Dopamine-Bench/jsons \
  --base_dir ./Robo-Dopamine-Bench/images \
  --out_root_dir ./eval_results/results_Robo-Dopamine-GRM-3B \
  --batch_size 16

2. Evaluate other models with API.

python -m eval.evaluation_api \
  --model_name <MODEL-NAME, e.g., gpt-4o, gemini-3-pro> \
  --api_key <OPENAI-API-KEY> \
  --base_url <OPENAI-BASE-URL> \
  --input_json_dir ./Robo-Dopamine-Bench/jsons \
  --base_dir ./Robo-Dopamine-Bench/images \
  --out_root_dir ./eval_results/results_{MODEL-NAME} \
  --max_workers 16