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 |
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. |
Quick Start
from datasets import load_dataset
dataset = load_dataset("macrodata/WGO-Bench", split="train")
example = dataset[0]
print(example["instruction"])
print(example["segments"])
To use the parquet directly:
import pandas as pd
df = pd.read_parquet("hf://datasets/macrodata/WGO-Bench/data/annotations.parquet")
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
- Refiner example: Subtask annotations
- Code: macrodata-labs/refiner