README.md

metadata

license: cc-by-4.0
task_categories:
  - video-classification
tags:
  - human-study
  - video-evaluation
  - embodied-ai

RoboJudge human study — annotation packages

16 self-contained, offline annotation packages covering the 800-item RoboJudge test set.

packages	items each	total
human_study_01..16.zip	50	800

Together: 800 items, no duplicates, full coverage. All clips belonging to the same (dataset, task, episode) scene stay inside one package, so a scene is never split across two annotators.

How to use

1. Download one zip and unpack it.
2. Double-click index.html — it runs from file://, no server needed.
3. Enter your name, score each clip, then click Save my results to export a JSON file.
4. Send that JSON back to whoever assigned you the package.

Progress is kept in localStorage, so closing the browser does not lose work — same machine, same browser. Opening the package on a different computer or browser starts from zero. An internet connection is required: the videos stream from the public dataset.

What is scored

Per clip, 8 inputs:

• Physical plausibility (PA), 1–5, with three sub-axes (0 fails / 1 partial / 2 ok): agent integrity · scene & object consistency · interaction realism
• Instruction alignment (IA), 1–5, with three sub-axes (0 / 1 / 2): right agent acts · right object · goal completed

No free text is collected.

Study design notes

These matter if you analyse the results:

• Annotators watch the full video at native frame rate. The automatic judges do not — they sample frames (the reference protocol is 4 fps). Annotators therefore see motion artefacts that frame-sampled judges cannot. Human and judge scores are not same-input measurements; treat the human scores as a reference standard rather than as an equal-footing comparison.
• No ground-truth reference video is shown, and the packages do not contain one.
• No init frame is shown. The judges receive video + instruction only, so showing an init frame would give annotators strictly more than the judge had.
• Packages carry only item_id, video_url and instruction — no gold labels and no model predictions, so scoring cannot be anchored by them.