metadata
license: cc-by-4.0
tags:
- vision-action
- desktop-agent
- os-task
- imitation-learning
- OWA
task_categories:
- robotics
OSTask-demo
A demo dataset of general OS task trajectories with synchronized video and input events. Each trajectory consists of a natural language instruction paired with a human demonstration recording.
Visualize
Explore recordings directly in your browser with synchronized keyboard/mouse overlay: 👉 Open in Dataset Visualizer
Task Domains
- Browser (Chromium): Cache cleanup, web navigation
- Office (Excel): Spreadsheet manipulation
- Code Editor (VS Code): Find and replace, text editing
- General OS: Application management, system operations
- Workflow: End-to-end tasks spanning multiple applications and tools
File Structure
Each task has three files:
chromium_01.json # Task instruction
chromium_01.mcap # Timestamped events + frame references
chromium_01.mkv # Video recording (HEVC)
JSON: Contains the natural language instruction for the task.
MCAP + MKV: Video and input events in OWAMcap format—keyboard, mouse, and screen capture with nanosecond timestamps.
Load the Data
pip install mcap-owa-support owa-msgs huggingface_hub
import json
from huggingface_hub import hf_hub_download
from mcap_owa.highlevel import OWAMcapReader
# Load instruction
json_file = hf_hub_download(
repo_id="open-world-agents/OSTask-demo",
filename="chromium_01.json",
repo_type="dataset"
)
instruction = json.load(open(json_file))["instruction"]
# Load trajectory
mcap_file = hf_hub_download(
repo_id="open-world-agents/OSTask-demo",
filename="chromium_01.mcap",
repo_type="dataset"
)
with OWAMcapReader(mcap_file) as reader:
for msg in reader.iter_messages(topics=["screen"]):
screen = msg.decoded
screen.resolve_relative_path(mcap_file)
frame = screen.load_frame_array() # numpy array (H, W, 4) RGBA
break
Usage
This dataset can be used for:
- Training desktop/OS automation agents
- Evaluating computer use models
- Research on GUI agents and multimodal agents