---
pattern_id: pattern_vlingnav_embodied_navigation_with_adaptive_reasoning_and_visual_assisted_linguis
applicable_symptoms: [vlingnav_embodied_navigation_with_adaptive_reasoning_and_visual_assisted_linguis]
domain: Planning_Decision
---

# VLN agents struggle with long-horizon tasks due to lack of explicit reasoning and memory of key visual features.

**Domain**: `Planning_Decision`

## Fix

AdaCoT generates language-based chain-of-thought for explicit reasoning, and VLingMem stores key visual features as linguistic memory; combined with VLA (LLaMA-7B+ViT-L) trained via online RL.

## Anti-pattern

Standard VLN methods without explicit reasoning or memory fail on long-horizon navigation.

## Cross-domain analogies

- **Perception_Vision** → Use a pre-trained vision-language model for shared embedding to enable zero-shot visual landmark memory.
  - related fix: Use a pre-trained vision-language model (e.g., CLIP) trained on large-scale image-text pairs to learn a shared embedding space, enabling zero-shot transfer to unseen tasks and objects.
- **Learning_Training** → Use group-relative advantage estimation to refine trajectory reasoning via sampled path comparisons.
  - related fix: Use GRPO (Group Relative Policy Optimization) as a second-stage RL fine-tuning after supervised chain-of-thought alignment, optimizing policy relative to a group of sampled trajectories via group-relative advantage estimation.
- **Control_Locomotion** → Distill multi-expert reasoning into a policy with DAgger and RL fine-tuning using visual memory.
  - related fix: Multi-expert distillation with DAgger and RL fine-tuning, using depth images as exteroceptive input.

## Patch

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

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

 # Symptom: VLN agents struggle with long-horizon tasks due to lack of explicit reasoning and memory of key visual features.

+# Fix    : AdaCoT generates language-based chain-of-thought for explicit reasoning, and VLingMem stores key visual features as linguistic memory; combined with VLA (LLaMA-7B+ViT-L) trained via online RL.

+# Avoid  : Standard VLN methods without explicit reasoning or memory fail on long-horizon navigation.

```
