Update WGO-Bench dataset card

README.md

@@ -1,5 +1,5 @@
 ---
-pretty_name: WASUP Subtask Annotations v1
+pretty_name: WGO-Bench
 task_categories:
 - robotics
 - video-classification
@@ -9,6 +9,8 @@ tags:
 - temporal-segmentation
 - subtask-annotation
 - robotics
+- robot-learning
+- egocentric-video
 - droid
 - robocoin
 - galaxea
@@ -18,22 +20,71 @@ size_categories:
 - n<1K
 ---
 
-# WASUP Subtask Annotations v1
+# WGO-Bench: What's Going On Benchmark
 
-Merged subtask-annotation dataset with videos embedded directly in the parquet.
+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.
 
-## Schema
+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
 
-- `id`: episode id
-- `video`: MP4 bytes embedded directly in the parquet
-- `instruction`: task instruction
-- `segments`: list of `{start_sec, end_sec, subtask}`
-- `metadata`: JSON string containing source-specific metadata that does not fit the common fields
+| Metric | Value |
+| --- | ---: |
+| Episodes | 100 |
+| Gold subtask segments | 605 |
+| Source families | 3 |
+| Format | Parquet |
+| Video storage | MP4 bytes embedded in each row |
 
 ## Sources
 
-- HomER: 25 rows
-- RoboInter DROID: 50 rows
-- RoboCOIN Galaxea R1 Lite head-camera: 25 rows
+| 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
+
+```python
+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:
+
+```python
+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
 
-Total: 100 rows, 605 subtask segments.
+- 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)