---
pattern_id: pattern_sensing_intelligence
applicable_symptoms: [sensing_intelligence]
domain: Perception_Vision
---

# Autonomous agents fail to interpret raw sensor data into actionable spatial information, leading to poor navigation decisions.

**Domain**: `Perception_Vision`

## Fix

Implement a sensing intelligence pipeline that fuses multiple sensor modalities (e.g., vision, depth, IMU) into a coherent representation, with attention mechanisms to prioritize salient input.

## Anti-pattern

Using raw sensor streams directly without fusion or attention, causing computational overload and delayed responses.

## Cross-domain analogies

- **Planning_Decision** → Use semantic foundation models to directly interpret raw sensor data into actionable spatial primitives.
  - related fix: Combine semantic reasoning with foundation models to enable zero-shot long-horizon navigation using only onboard sensing.
- **Learning_Training** → Use realistic simulation benchmarks with hierarchical perception primitives to bridge raw sensor data and actionable spatial cues.
  - related fix: Use IsaacLab simulation benchmark with realistic scenes and low-level control primitives to evaluate and transfer navigation policies to real-world robots
- **Control_Locomotion** → Closed-loop verification of local depth features against global semantic priors enables real-time spatial interpretation.
  - related fix: Closed-loop controller that reconciles a local metric map with high-level navigation commands, generating continuous local trajectories from monocular depth and traversability estimates in real-time.

## Patch

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

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

 # Symptom: Autonomous agents fail to interpret raw sensor data into actionable spatial information, leading to poor navigation decisions.

+# Fix    : Implement a sensing intelligence pipeline that fuses multiple sensor modalities (e.g., vision, depth, IMU) into a coherent representation, with attention mechanisms to prioritize salient input.

+# Avoid  : Using raw sensor streams directly without fusion or attention, causing computational overload and delayed responses.

```
