---
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.