---
pattern_id: pattern_visual_navigation_policy
applicable_symptoms: [visual_navigation_policy]
domain: Planning_Decision
---

# Visual navigation policies fail to generalize to novel environments when trained on limited visual data, and explicit geometric modeling struggles with unstructured scenes.

**Domain**: `Planning_Decision`

## Fix

Train a deep neural network visuomotor policy that maps egocentric camera images directly to velocity commands, using diverse simulation data for zero-shot generalization.

## Anti-pattern

Classical geometric navigation pipelines (feature extraction, structure from motion) that require explicit maps or depth sensors.

## Cross-domain analogies

- **Perception_Vision** → Train a policy on simulated environments with varied visuals to improve generalization.
  - related fix: Use deep learning models (e.g., CNNs) trained on simulated galaxy merger images to automatically classify merger stages.
- **Learning_Training** → Bootstrapping with imitation from limited visual data, then refining with RL for novel environments.
  - related fix: Mixed Imitation and Reinforcement Learning (MIRL): bootstrap policy via off-policy imitation learning, then refine with on-policy RL, gradually shifting weight from imitation to RL.
- **Control_Locomotion** → Use standardized benchmark tasks requiring precise geometric reasoning for visual navigation training.
  - related fix: Use EB-Manipulation benchmark to evaluate and train agents on low-level actions (joint torques, end-effector poses) with standardized tasks that require precise perception and spatial reasoning.

## Patch

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

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

 # Symptom: Visual navigation policies fail to generalize to novel environments when trained on limited visual data, and explicit geometric modeling struggles with unstructured scenes.

+# Fix    : Train a deep neural network visuomotor policy that maps egocentric camera images directly to velocity commands, using diverse simulation data for zero-shot generalization.

+# Avoid  : Classical geometric navigation pipelines (feature extraction, structure from motion) that require explicit maps or depth sensors.

```
