README.md

metadata

task_categories:
  - reinforcement-learning
language:
  - en
tags:
  - embodied-ai
  - world-models
  - navigation
  - openapps
  - computer-vision
size_categories:
  - 10K<n<100K

OpenApps Scripted Navigation (3,024 episodes, 20 routes)

Inter-app navigation trajectories in OpenApps, collected with a scripted policy using only real UI actions (click "Return to List of Apps", click the target app icon) — no goto() teleports, so transitions are learnable and plannable from the action space alone.

• 20 routes: 5 source apps (todo, calendar, messages, codeeditor, map) × 4 targets
• 3,024 episodes after filtering (from 4,000 collected), 20 steps each
• Episode structure: ~10 random scrolls in the source app, click Return (home screen), click the target app icon, then random scrolls in the target app
• pixels: 1024×640 RGB (JPEG)
• action: [type, grid_x, grid_y], where type 0 = click, 1 = scroll-down, 2 = scroll-up on a 32×20 grid
• task: navigate_from_{src}_to_{tgt}
• No reward column (scripted policy, not agent-collected)

Collector: tools/nav_policy.py in the OpenApps data collection codebase.

Note: content uses OpenApps' canonical fixed app state with theme/language variation. For randomized underlying app state with ground-truth labels, see the companion openapps-todo-proprio-300ep dataset.