| --- |
| pretty_name: WGO-Bench |
| task_categories: |
| - robotics |
| - video-classification |
| language: |
| - en |
| tags: |
| - temporal-segmentation |
| - subtask-annotation |
| - robotics |
| - robot-learning |
| - egocentric-video |
| - droid |
| - robocoin |
| - galaxea |
| - homer |
| license: cc-by-nc-sa-4.0 |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/annotations.parquet |
| --- |
| |
| # WGO-Bench: What's Going On Benchmark |
|
|
| WGO-Bench is a small, manually annotated benchmark for evaluating how well vision-language models can turn robot and egocentric manipulation videos into timestamped subtask annotations. |
|
|
| Each row contains one video episode, a high-level task instruction, and gold subtask segments with start time, end time, and a concise action label. The benchmark is designed for two related tasks: |
|
|
| - **Boundary detection:** predict where one meaningful manipulation subtask ends and the next begins. |
| - **Subtask labeling:** given a fixed segment, describe the completed manipulation event. |
|
|
| ## Dataset Summary |
|
|
| | Metric | Value | |
| | --- | ---: | |
| | Episodes | 100 | |
| | Gold subtask segments | 743 | |
| | Unique task instructions | 63 | |
| | Source families | 3 | |
| | Format | Parquet | |
| | Video storage | MP4 bytes embedded in each row | |
| | Robot-state coverage | 75 robotic episodes; HomER is video-only | |
|
|
| ## Sources |
|
|
| | Source | Episodes | |
| | --- | ---: | |
| | HomER egocentric videos | 25 | |
| | RoboInter DROID station-arm videos | 50 | |
| | RoboCOIN Galaxea R1 Lite head-camera videos | 25 | |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| | --- | --- | --- | |
| | `id` | string | Stable episode identifier. | |
| | `video` | binary | MP4 video bytes embedded directly in the parquet row. | |
| | `instruction` | string | High-level task instruction for the episode. | |
| | `segments` | list | Gold `{start_sec, end_sec, subtask}` annotations. | |
| | `metadata` | string | JSON metadata with source-specific fields. | |
| | `perception_state` | struct or null | Synchronized, named robot-state channels for DROID/Galaxea; null for HomER. | |
|
|
| DROID retains EEF position, rotation, and gripper state. Galaxea retains |
| bilateral arm joints and grippers plus its available EEF- and gripper-derived |
| channels. Actions are not included. HomER has no robot telemetry, so its rows |
| contain `null` rather than synthetic zeros. |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("macrodata/WGO-Bench", split="train") |
| example = dataset[0] |
| |
| print(example["instruction"]) |
| print(example["segments"]) |
| print(example["perception_state"]) |
| ``` |
|
|
| To use the parquet directly: |
|
|
| ```python |
| import pandas as pd |
| |
| df = pd.read_parquet("hf://datasets/macrodata/WGO-Bench/data/annotations.parquet") |
| ``` |
|
|
| ## Rebuilding Robot States |
|
|
| `scripts/perception_sources.lock.json` pins both annotation maps and all 26 |
| upstream robot-dataset revisions. Rebuild the enriched table from the original |
| annotations Parquet: |
|
|
| ```bash |
| python scripts/embed_perception_states.py \ |
| data/annotations.original.parquet \ |
| data/annotations.parquet |
| ``` |
|
|
| Requirements are Python 3.11+, `huggingface_hub`, and `pyarrow`. The exporter |
| validates episode coverage, frame counts, channel widths, and timestamp order, |
| then writes a SHA-256 provenance file next to the enriched Parquet. Hugging Face |
| caches all state sources; pass `--cache-dir` to use a shared cache. The DROID |
| state data comes from one roughly 241 MB upstream tar archive. The embedded |
| state payload adds roughly 1.5 MB before Parquet-level recompression. |
|
|
| ## Annotation Policy |
|
|
| Segments are intended to describe completed manipulation events, not every small pose adjustment. A new segment should generally correspond to a visible state change such as picking up, placing, opening, closing, moving, pouring, wiping, cutting, or transferring an object. |
|
|
| The labels are short action descriptions grounded in what is visible in the clip. They are meant to be useful for downstream policy training, reward modeling, and evaluating automatic subtask annotation pipelines. |
|
|
| ## Related Resources |
|
|
| - Blog post: [Segmenting Robot Video into Actionable Subtasks](https://macrodata.co/blog/inferring-robot-subtasks-from-video) |
| - Refiner example: [Subtask annotations](https://macrodata.co/docs/examples/annotations/subtask-annotations) |
| - Code: [macrodata-labs/refiner](https://github.com/macrodata-labs/refiner) |
|
|
