---
pattern_id: pattern_vla_an
applicable_symptoms: [vla_an]
domain: Planning_Decision
---

# VLA-based drone navigation suffers from domain gap between simulation and reality, insufficient temporal reasoning, safety risks from generative action policies, and onboard deployment constraints on resource-limited UAVs.

**Domain**: `Planning_Decision`

## Fix

Integrate 3D Gaussian Splatting scene representation, Progressive Three-Stage Training Framework, and Geometric Safety Correction Module for collision-free command generation.

## Anti-pattern

Prior VLA navigation methods using standard sim-to-real data and generative policies without geometric constraints.

## Cross-domain analogies

- **Perception_Vision** → Voxelize temporal action space into structured 3D cells for multi-task safety prediction.
  - related fix: Voxelize the physical world into structured 3D cells and aggregate multi-view 2D features into that unified 3D space via 2D-3D spatial sampling, then jointly predict 3D occupancy, room layout, and bounding boxes through multi-task learning.
- **Learning_Training** → Train a closed-loop verification loop between a simulator and onboard planner to enforce action-path consistency.
  - related fix: Train a Transformer-based Speaker jointly with a Listener in a Double Back-Translation loop, where the Speaker generates instructions from paths and the Listener reconstructs paths from instructions, enforcing instruction-path consistency through iterative refinement.
- **Control_Locomotion** → Use terrain-aware closed-loop adaptation to ground VLA actions in real-time sensor feedback.
  - related fix: Incorporate terrain information (depth images or elevation maps) into the control policy to dynamically adapt gait, foothold placement, and body posture.

## Patch

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

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

 # Symptom: VLA-based drone navigation suffers from domain gap between simulation and reality, insufficient temporal reasoning, safety risks from generative action policies, and onboard deployment constraints on resource-limited UAVs.

+# Fix    : Integrate 3D Gaussian Splatting scene representation, Progressive Three-Stage Training Framework, and Geometric Safety Correction Module for collision-free command generation.

+# Avoid  : Prior VLA navigation methods using standard sim-to-real data and generative policies without geometric constraints.

```
