shi-labs/physical-ai-bench-leaderboard

Physical AI Bench Leaderboard

Physical AI Bench (PAI-Bench) is a comprehensive benchmark suite for evaluating physical AI generation and understanding across diverse scenarios including autonomous vehicles, robotics, industrial spaces, and everyday ego-centric environments.

Resources

• —🌐 GitHub Repository
• —📊 Predict Dataset
• —📊 Transfer Dataset
• —📊 Reason Dataset

Citation

@misc{PAIBench2025,
  title={Physical AI Bench: A Comprehensive Benchmark for Physical AI Generation and Understanding},
  author={Fengzhe Zhou and Jiannan Huang and Jialuo Li and Humphrey Shi},
  year={2025},
  url={https://github.com/SHI-Labs/physical-ai-bench}
}

Configuration

Most of the variables to change for a default leaderboard are in src/env.py (replace the path for your leaderboard) and src/about.py (for tasks).

Results files should have the following format and be stored as json files:

{
    "config": {
        "model_dtype": "torch.float16", # or torch.bfloat16 or 8bit or 4bit
        "model_name": "path of the model on the hub: org/model",
        "model_sha": "revision on the hub",
    },
    "results": {
        "task_name": {
            "metric_name": score,
        },
        "task_name2": {
            "metric_name": score,
        }
    }
}

Request files are created automatically by this tool.

If you encounter problem on the space, don't hesitate to restart it to remove the create eval-queue, eval-queue-bk, eval-results and eval-results-bk created folder.

Code logic for more complex edits

You'll find

• —the main table' columns names and properties in src/display/utils.py
• —the logic to read all results and request files, then convert them in dataframe lines, in src/leaderboard/read_evals.py, and src/populate.py
• —the logic to allow or filter submissions in src/submission/submit.py and src/submission/check_validity.py