RoboPulse

RoboPulse is a benchmark introduced in PRM-as-a-Judge: A Dense Evaluation Paradigm for Fine-Grained Robotic Auditing for testing whether a vision-language judge can detect fine-grained relative progress in physical manipulation.

This Hugging Face release contains the hard 1800-example subset. Each example asks the judge to compare a BEFORE state and an AFTER state under the same task, while using task-start and task-end reference frames as anchors for the full task scope.

Overview

The figure below illustrates the multi-view comparison setup in RoboPulse.

Files

• RoboPulse.json: benchmark annotations with release-relative image paths
• images.zip: zipped image assets
• README.md: dataset overview and field definitions
• robopulse_vis.png: multi-view comparison illustration
• robopulse_stat.png: dataset coverage statistics
• results.png: benchmark results table from the paper

If you want the image paths in RoboPulse.json to resolve locally, extract images.zip in the same folder so that the images/ directory sits next to RoboPulse.json.

Dataset Summary

• Number of samples: 1800
• Image references in JSON: 14400
• Unique image files: 13059
• Total source image size: 345.72 MB
• Archive size: 340.97 MB
• Source datasets: 9
• Hop magnitude bins: small, medium, large

Source datasets in this release:

• agibotworld: 200 samples
• agilex_newdragon: 200 samples
• droid_oxe: 200 samples
• galaxea_r1lite: 200 samples
• human_egodex: 200 samples
• human_pika: 200 samples
• libero_data: 200 samples
• robocasa_data: 200 samples
• robotwin2_agilex_part1: 200 samples

The figure below summarizes the coverage of RoboPulse across data sources and task semantics.

Results

The figure below shows the main pairwise progress-judgment results reported for RoboPulse.

Data Format

Each item in RoboPulse.json is a dictionary with the following fields:

• id: unique sample identifier
• task: task instruction for the sample
• image_dataset: source dataset name
• image: a list of 8 image paths, all relative to this release folder
• conversations: question-answer style supervision for the judge
• hop_value: signed Hop value used to construct the sample pair
• hop_absolute_value: absolute value of hop_value
• hop_category: categorical metadata derived from hop_value

Image Ordering

image[0] to image[7] always follow the same order:

1. image[0]: reference start frame for the task
2. image[1]: reference end frame for the completed task
3. image[2]: front view of the BEFORE state
4. image[3]: left wrist view of the BEFORE state
5. image[4]: right wrist view of the BEFORE state
6. image[5]: front view of the AFTER state
7. image[6]: left wrist view of the AFTER state
8. image[7]: right wrist view of the AFTER state

In other words, the benchmark compares a BEFORE triplet against an AFTER triplet, with start and end reference frames provided as conceptual anchors.

Conversations

conversations stores the judge prompt and the target answer:

• conversations[0]: the evaluation question given to the judge model
• conversations[1]: the expected answer, such as <score>+1</score> for progress and <score>-1</score> for regression

Hop Fields

The Hop-based fields describe the relative progress signal used to build RoboPulse. For the detailed formulation, please refer to Appendix F of the paper:

• Paper: PRM-as-a-Judge
• PDF: https://arxiv.org/pdf/2603.21669

Field meanings:

• hop_value: signed relative progress change between the two compared states. Positive values indicate forward progress toward the task goal, while negative values indicate regression away from the goal.
• hop_absolute_value: magnitude of the progress change, ignoring direction.
• hop_category: a dictionary with three subfields: absolute_category, direction, and combined_category
• absolute_category: magnitude bucket of the Hop value, one of small, medium, or large
• direction: direction bucket, either progress (forward) or regression (backward)
• combined_category: combination of the two, such as progress_small, progress_medium, progress_large, regression_small, regression_medium, or regression_large

Directory Layout

After extracting images.zip, the folder should look like this:

hf_RoboPulse/
├── RoboPulse.json
├── images.zip
├── README.md
└── images/
    └── <dataset_name>/
        └── ...

Usage Notes

• Upload the whole folder to your Hugging Face dataset repository.
• If you want image paths in the JSON to be directly readable from the repo, extract images.zip before or after uploading so that images/ exists alongside RoboPulse.json.
• The release preserves the original benchmark annotations and only rewrites image paths to release-relative paths under images/.

Related Links

• Project repository: PRM-as-a-Judge
• Paper: PRM-as-a-Judge: A Dense Evaluation Paradigm for Fine-Grained Robotic Auditing

Citation

If this project, leaderboard, or evaluation pipeline helps your work, please cite:

@article{ji2026prmjudge,
  title = {PRM-as-a-Judge: A Dense Evaluation Paradigm for Fine-Grained Robotic Auditing},
  author = {Ji, Yuheng and Liu, Yuyang and Tan, Huajie and Huang, Xuchuan and Huang, Fanding and Xu, Yijie and Chi, Cheng and Zhao, Yuting and Lyu, Huaihai and Co, Peterson and others},
  journal = {arXiv preprint arXiv:2603.21669},
  year = {2026}
}