README.md

metadata

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:

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.