README.md

---
license: other
language:
- en
pretty_name: ProcessBench
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: data/processdata_sft.parquet
  - split: eval
    path: data/processbench_eval.parquet
---

# ProcessBench

## Dataset Summary

ProcessBench 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

ProcessBench 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

This release is organized around a minimal reviewable benchmark package:

- `splits/`: SFT and Eval QA entries.
- `metadata/`: split statistics, task distribution, and license notes.
- `examples/`: rendered representative task cards examples.
- `ProcessData-SFT-Qwen/`: ProcessData-SFT-Qwen LoRA adapter weights and configurations.
- `ProcessData-SFT-Qwen_results/`:  ProcessData-SFT-Qwen predictions and score summaries.
- `benchmark_card.md`: benchmark-level documentation.
- `croissant.json`: Croissant metadata with core and Responsible AI fields.

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

## SFT-Eval Split Reconstruction

### Relevant Assets

- Public QA rows for `eval` and `sft`.
- Split manifests and split counts.
- Public release-relative references for each item.

### How to use the public references

Each row exposes three reconstruction-oriented fields:

- `visual_ref`
- `source_episode_ref`
- `reconstruction_key_json`

These are designed to be stable, path-free references. They are sufficient to map a released item back to the corresponding source episode or recording once you have obtained the upstream dataset under its original terms.

### Source-specific units

- `GM-100`: grouped by `(task_id, episode_id)`.
- `RH20T`: grouped by `recording_id`.
- `REASSEMBLE`: grouped by `recording_id`.
- `AIST-Bimanual`: grouped by `recording_id`.

### Expected workflow

1. Obtain upstream datasets under their original terms.
2. Use `source`, `source_task_id`, `source_unit_id`, `camera`, and `frame_indices_json` from the public release.
3. Reconstruct the required visual inputs with the source-specific evaluation scripts in the main codebase.


## Public QA fields Schema

- `item_id`: Stable public item identifier.
- `split`: `eval` or `sft`.
- `source`: `GM-100`, `RH20T`, `REASSEMBLE`, or `AIST-Bimanual`.
- `source_slug`: Short source slug used in release-relative references.
- `source_task_id`: Source-side task identifier without local paths.
- `source_unit_type`: Split-isolation unit type, e.g. `episode` or `recording`.
- `source_unit_id`: Public unit identifier without local paths.
- `task_id`: Canonical paper task ID `T1` ... `T12`.
- `task_name`: Canonical paper task name.
- `task_type_legacy`: Legacy engineering task name when it differs from paper naming.
- `input_type`: Public input format description.
- `question`: Public question text.
- `choice_A` ... `choice_F`: Flattened answer choices.
- `answer`: Public answer label in the release schema.
- `answer_text`: Public answer text mapped from `answer`.
- `num_choices`: Number of non-null answer choices.
- `num_frames`: Number of referenced frames.
- `frame_indices_json`: JSON-encoded frame indices.
- `display_labels_json`: JSON-encoded panel labels such as `["X", "Y"]` or `["X", "Z", "Y"]`.
- `camera`: Source camera identifier when available.
- `arm_type`: Source-side arm configuration tag when available.
- `visual_ref`: Release-relative visual reference string. This is not a local filesystem path.
- `source_episode_ref`: Release-relative source unit reference.
- `reconstruction_key_json`: Minimal JSON payload needed to reconstruct the sample with upstream data and source-specific scripts.
- `task_meta_in_source`: Whether source-side task metadata/context exists upstream.
- `task_meta_public`: Always `false` in this release build. Separate structured task-meta fields are intentionally withheld.
- `split_version`: Current split version.
- `split_group_id`: Source-side split group identifier.
- `builder_version`: Public builder version tag.
- `prompt_version`: Public prompt-serialization version tag.
- `sft_target`: SFT-only supervised target string. Present only in `processdata_sft.*`.

### Notes

- `T5` is equivalent to `T_progress`.
- `T8` is equivalent to `T_temporal`.
- `T9` is equivalent to `T_binary`.
- `T8` is normalized into six explicit permutation choices so the public table remains flat and Parquet-friendly.
- `T9` is normalized to `A/B` choices even when the source data originally used `X/Y`.



## Prompt Templates

This file documents the public prompt serialization used by the current release build. Separate source-side task-meta prompting is intentionally disabled in this public release.

### Standard multiple-choice template

```text
{question}

A: {choice_A}
B: {choice_B}
...

Choose exactly one option label.

Output protocol:
- You may include brief reasoning before the final answer.
- The final line must be exactly: <ANSWER>A</ANSWER>
- Do not output anything after </ANSWER>.
```

### `T8` Temporal Ordering template

```text
You are shown 3 frames from a robot manipulation task.
The frames are labeled X, Y, Z (these labels are arbitrary identifiers, not positional, and not time-ordered).
Determine the correct chronological order of these frames (from earliest to latest).
Choose exactly one 3-letter permutation, for example: YXZ means Y happened first, then X, then Z.

Output protocol:
- You may include brief reasoning before the final answer.
- The final line must be exactly: <ANSWER>XYZ</ANSWER>
- Do not output anything after </ANSWER>.
```

### `T9` Temporal Priority template

```text
A single comparison image shows two labeled robot-manipulation panels from the same episode.
The left-right placement of the panels and the labels are arbitrary identifiers and do not indicate temporal order.
Which labeled panel happened earlier in the real manipulation sequence, X or Y?
Choose exactly one label: X or Y.

Output protocol:
- You may include brief reasoning before the final answer.
- The final line must be exactly: <ANSWER>X</ANSWER>
- Do not output anything after </ANSWER>.
```


## License and Terms

This release uses `license: other`.

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