---
pattern_id: pattern_mm_nav_multi_view_vla_model_for_robust_visual_navigation_via_multi_expert_learni
applicable_symptoms: [mm_nav_multi_view_vla_model_for_robust_visual_navigation_via_multi_expert_learni]
domain: Planning_Decision
---

# Single-view VLA models fail in cluttered environments due to limited field-of-view and lack of task-specific behaviors.

**Domain**: `Planning_Decision`

## Fix

Train three RL experts (reaching, squeezing, avoiding) and fine-tune a VLA model (SigLIP+Qwen2-7B) with multi-expert learning, then deploy with online teacher-student training using 4 fisheye cameras on Unitree GO2.

## Anti-pattern

Using a single-view VLA model without task-specific expert policies.

## Cross-domain analogies

- **Perception_Vision** → Augment single-view training with synthetic multi-view and task-specific occlusion data.
  - related fix: Generate synthetic depth images with simulated sensor noise, self-occlusion, and lighting effects to match real sensor distribution.
- **Learning_Training** → Distribute multi-view VLA inference across parallel workers with synchronized behavior fusion.
  - related fix: Distribute PPO training across multiple workers with synchronized gradient updates (DD-PPO).
- **Control_Locomotion** → Distill multi-expert policies from multiple camera views with DAgger and fine-tune with task-specific rewards.
  - related fix: Multi-expert distillation with DAgger and RL fine-tuning, using depth images as exteroceptive input.

## Patch

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

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

 # Symptom: Single-view VLA models fail in cluttered environments due to limited field-of-view and lack of task-specific behaviors.

+# Fix    : Train three RL experts (reaching, squeezing, avoiding) and fine-tune a VLA model (SigLIP+Qwen2-7B) with multi-expert learning, then deploy with online teacher-student training using 4 fisheye cameras on Unitree GO2.

+# Avoid  : Using a single-view VLA model without task-specific expert policies.

```
