---
pattern_id: pattern_go_stanford
applicable_symptoms: [go_stanford]
domain: Planning_Decision
---

# Visual navigation agents fail to generalize to unseen indoor environments in zero-shot settings, with high trajectory error and low success rate.

**Domain**: `Planning_Decision`

## Fix

Use a unified world model (UniWM) trained on diverse navigation benchmarks including Go Stanford for zero-shot transfer.

## Anti-pattern

Task-specific models that overfit to training environments and fail on unseen scenes.

## Cross-domain analogies

- **Perception_Vision** → Fuse visual and geometric cues into a unified spatial representation for zero-shot navigation.
  - related fix: Propose a multimodal occupancy perception system that fuses vision, depth, and other sensor data into a unified occupancy representation for humanoid robots.
- **Learning_Training** → Apply multi-scale dropout to visual features to prevent overfitting to training environments.
  - related fix: Apply dropout operations at multiple feature scales (activations, channels, spatial regions, entire feature maps) to regularize training.
- **Control_Locomotion** → Train end-to-end policy with domain-randomized simulation to map raw observations directly to actions.
  - related fix: Train a single end-to-end neural network policy via large-scale RL in simulation with domain randomization, mapping depth image directly to motor commands

## Patch

```diff
--- go_stanford.before.py
+++ go_stanford.after.py
@@ -1,2 +1,4 @@
-# --- BEFORE (vulnerable to the symptom below) ---

+# --- AFTER (ROSCLAW heuristic graft) ---

 # Symptom: Visual navigation agents fail to generalize to unseen indoor environments in zero-shot settings, with high trajectory error and low success rate.

+# Fix    : Use a unified world model (UniWM) trained on diverse navigation benchmarks including Go Stanford for zero-shot transfer.

+# Avoid  : Task-specific models that overfit to training environments and fail on unseen scenes.

```
