AI Robot Decision Explainer

explains robot decisions in understandable terms • shows key factors influencing AI behavior • displays confidence scores and reasoning • visualizes decision pathways used by the AI model

Robot Scenario & Inputs

Select Scenario:

Environment Data (JSON):

AI Model Output (Logits):

Decision Explanation

Final Decision: STOP

The robot stopped because a red traffic light and a nearby pedestrian were detected. Confidence in this decision is high.

Action Confidence Scores

Stop 85%

Go 10%

Slow Down 5%

🔑 Key Factors Influencing Decision

Traffic Light (Red) 65%
Pedestrian Nearby 25%
Obstacle Distance 10%

🔄 Decision Pathway

Sensor Fusion

LIDAR, Camera, GPS data aggregated

LIDAR: 2.5mCamera: PedestrianTraffic: Red

Feature Extraction

Key features identified: traffic light state, pedestrian proximity

Model Inference (Neural Network)

Processed through 3 hidden layers

Action Selection

Highest confidence action: STOP (85%)

Final Decision: STOP

How To Use AI Robot Decision Explainer

📝 Step 1: Choose Scenario

Select a predefined robot scenario (Navigation, Pick & Place, Collision Avoidance) to load default sensor data.

⚙️ Step 2: Review Sensor & Model Data

The tool shows simulated environment data (LIDAR, camera, GPS) and raw AI model outputs (logits/probabilities). You can edit the JSON to test different situations.

🔧 Step 3: Analyze Decision

Click "Analyze Decision" to process the inputs. The AI Explainer interprets the model's output and sensor data to generate a human-readable explanation.

📋 Step 4: Interpret Results

Review the decision summary, confidence scores, key influencing factors, and the step-by-step decision pathway. Use the "Copy" button to save the explanation.

💡 Pro Tips

Modify the environment JSON to simulate edge cases (e.g., sensor failure).
Compare scenarios to see how feature importance changes.
The pathway visualization helps debug unexpected AI behavior.
Ideal for safety audits and building trust in autonomous systems.

🔍 Example

Scenario: Robot approaches a crosswalk.

Input: Camera detects red light + pedestrian. LIDAR shows clear path.

Explanation: "Robot STOPs (85% confidence). Primary factor: Red traffic light (65% influence). Secondary: Pedestrian proximity (25%)."

Frequently Asked Questions

What types of AI models does this work with?

It's designed for classification and decision-making models common in robotics: neural networks, decision trees, and reinforcement learning policies. It uses feature attribution (like SHAP values) and confidence scores from the model's output.

Is this a real-time explanation tool?

This demo processes static inputs. In a real robotic system, a similar pipeline would run in near real-time, logging decisions and their explanations for operator review or post-mission analysis.

What are "decision pathways"?

They are simplified visualizations of the data flow and inference steps: from raw sensor input → feature extraction → model inference → final action. This helps identify at which stage a particular factor influenced the outcome.

How are the key influencing factors calculated?

The explainer uses the feature importance weights provided in the simulated AI model output. In a real system, these could come from techniques like Integrated Gradients, LIME, or attention mechanisms in the model architecture.

Why is this important for safety-critical environments?

In domains like autonomous driving or healthcare, operators and regulators need to trust AI decisions. An explainer provides transparency, helps verify that the AI is focusing on relevant factors (e.g., a red light vs. a shadow), and aids in debugging failures.

Can I integrate this with my own robot?

Absolutely. The tool's logic (PHP/JS) is a front-end demo. For integration, you would replace the simulated data with real-time feeds from your robot's sensors and AI model, and feed them into a similar explanation engine on your backend or edge device.