README.md

metadata

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

{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

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

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.