---
pattern_id: pattern_open_architecture_end_to_end_navigation_system
applicable_symptoms: [open_architecture_end_to_end_navigation_system]
domain: Planning_Decision
---

# End-to-end navigation systems fail in unseen environments due to lack of semantic understanding and inability to adapt plans in real time.

**Domain**: `Planning_Decision`

## Fix

Integrate hierarchical scene graph construction with an LLM-based planner in a ROS2 framework for zero-shot, real-time goal-oriented navigation.

## Anti-pattern

Traditional end-to-end navigation systems that rely on pre-trained models and lack open-vocabulary semantic scene understanding.

## Cross-domain analogies

- **Perception_Vision** → Use pinhole projection to map latent plans into a consistent semantic action frame.
  - related fix: Use pinhole camera projection model with intrinsic matrix K and extrinsic matrix [R|t] to map 3D world points to 2D image coordinates, enabling local-to-world transformations.
- **Learning_Training** → Distill privileged semantic and plan adaptation into student via guidance loss.
  - related fix: Privileged Information Guidance (PIG): train a diffusion policy with privileged depth and collision information during training, then distill into a student policy that uses only RGB observations via a guidance loss.
- **Control_Locomotion** → Train a single policy with domain randomization and sim-to-real transfer for robust real-time adaptation.
  - related fix: Train a single reinforcement-learning-based policy that directly maps noisy depth images to motor commands, using domain randomization and sim-to-real transfer to handle sensor artifacts and actuation imprecision.

## Patch

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

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

 # Symptom: End-to-end navigation systems fail in unseen environments due to lack of semantic understanding and inability to adapt plans in real time.

+# Fix    : Integrate hierarchical scene graph construction with an LLM-based planner in a ROS2 framework for zero-shot, real-time goal-oriented navigation.

+# Avoid  : Traditional end-to-end navigation systems that rely on pre-trained models and lack open-vocabulary semantic scene understanding.

```
