README.md

---
license: other
language:
- en
pretty_name: RoboProcessBench
task_categories:
- visual-question-answering
tags:
- robotics
- embodied-ai
- benchmark
- vision-language-models
- process-understanding
- manipulation
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: splits/processdata_sft.parquet
  - split: eval
    path: splits/processdata_eval.parquet
---

# RoboProcessBench

## Dataset Summary

RoboProcessBench is a process-aware benchmark for vision-language robotic manipulation understanding. It evaluates whether VLMs can infer how a manipulation execution unfolds, including phase, contact, motion, bimanual coordination, primitive-local progress, temporal order, outcome, and primitive-level transitions.

This release contains 57,892 QA rows: 48,841 SFT rows and 9,051 evaluation rows across 12 task families and 260 manipulation tasks. The split follows strict episode / recording / scene isolation. The benchmark is derived from GM-100, RH20T, REASSEMBLE, and AIST-Bimanual.

## Task Families

RoboProcessBench contains 12 diagnostic question families:

- **T1 Phase Recognition:** identify the current coarse process phase.
- **T2 Contact Detection:** determine whether task-relevant contact has occurred.
- **T3 Motion Direction Prediction:** infer the dominant motion direction from short temporal context.
- **T4 Bimanual Coordination State:** identify the current coordination state of two arms.
- **T5 Primitive-local Progress:** estimate progress within the current local manipulation step.
- **T6 Motion State Recognition:** distinguish actively moving from stationary states.
- **T7 Operation Outcome Prediction:** predict eventual success or failure from partial execution evidence.
- **T8 Temporal Ordering:** reconstruct the chronological order of shuffled observations.
- **T9 Temporal Priority Prediction:** decide which of two observations occurred earlier.
- **T10 Current Primitive Recognition:** identify the current low-level primitive.
- **T11 Next Primitive Prediction:** infer the next primitive from local process context.
- **T12 Primitive Chain Restoration:** restore a masked primitive in a local primitive chain.


## Repository Contents

The release is organized as a compact, reviewable benchmark package:

```text
RoboProcessBench/
├── splits/                         # SFT and Eval QA entries, split summaries
│   ├── processdata_sft.jsonl
│   ├── processdata_sft.parquet
│   ├── sft_manifest.jsonl
│   ├── processdata_eval.jsonl
│   ├── processdata_eval.parquet
│   └── eval-manifest.jsonl 
│
├── metadata/                       # statistics, schema, licenses, prompt templates, reconstruction notes
│   ├── split_summary.json
│   ├── task_distribution.csv
│   ├── asset_licenses.csv
│   ├── schema.md
│   ├── prompt_templates.md
│   └── reconstruction.md
│
├── examples/                       # rendered representative benchmark cards
│   └── task_cards/
│       ├── T1_phase_recognition.png
│       ├── T2_contact_detection.png
│       ├── ...
│       └── T12_primitive_chain_restoration.png
│
├── ProcessData-SFT-Qwen/           # LoRA adapter weights and training configuration
│   ├── adapter_config.json
│   ├── adapter_model.safetensors
│   ├── ...
│   └── training_config.json
│
├── ProcessData-SFT-Qwen_results/   # predictions and summary of the post-trained model 
│   ├── ProcessData-SFT-Qwen_predictions.json
│   └── ProcessData-SFT-Qwen_summary.json
│
├── benchmark_card.md               # benchmark-level documentation
├── croissant.json                  # Croissant core + Responsible AI metadata
└── README.md
```

Full upstream videos and full frame dumps are not redistributed in this release.


## License and Terms

This release uses `license: other`.

- Derived benchmark metadata in this release remains subject to upstream dataset terms.