---
pattern_id: pattern_object_level_topometric_navigation
applicable_symptoms: [object_level_topometric_navigation]
domain: Planning_Decision
---

# Mobile robot navigation fails to generalize to novel environments when goals are specified as raw metric coordinates or topological node sequences, because these representations lack semantic grounding and cannot adapt to unseen object layouts.

**Domain**: `Planning_Decision`

## Fix

Represent navigation goals as semantic object landmarks, maintain a topological graph where nodes are associated with detected object instances and edges encode metric spatial relationships, enabling symbolic goal reasoning with precise metric execution.

## Anti-pattern

Using purely metric maps or topological node sequences without object-level anchoring.

## Cross-domain analogies

- **Perception_Vision** → Use diffusion to generate semantic goal imaginations from coordinate inputs for robust grounding.
  - related fix: Generate synthetic visual imaginations from segmented instruction phrases using a text-to-image diffusion model, and train with an auxiliary loss that aligns imaginations with their corresponding referring expressions.
- **Learning_Training** → Use self-supervised pseudo-label generation to create semantic goal representations from unlabeled environment observations.
  - related fix: Use unsupervised adversarial training with self-supervised learning (e.g., rotation prediction) to generate pseudo-labels for robust training against adversarial perturbations.
- **Control_Locomotion** → Train an end-to-end neural policy mapping raw sensor data directly to goal-directed actions.
  - related fix: Train a single neural network policy via deep reinforcement learning that maps raw depth camera images directly to motor commands, bypassing hand-crafted perception and control layers.

## Patch

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

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

 # Symptom: Mobile robot navigation fails to generalize to novel environments when goals are specified as raw metric coordinates or topological node sequences, because these representations lack semantic grounding and cannot adapt to unseen object layouts.

+# Fix    : Represent navigation goals as semantic object landmarks, maintain a topological graph where nodes are associated with detected object instances and edges encode metric spatial relationships, enabling symbolic goal reasoning with precise metric execution.

+# Avoid  : Using purely metric maps or topological node sequences without object-level anchoring.

```
