How AI Decision Explainer Works
1 Configure Robot Scenario
Select a predefined scenario (Navigation, Pick & Place, Collision Avoidance) or customize sensor data and AI model outputs to simulate different environmental conditions.
2 AI Model Processing
The explainer analyzes sensor fusion data and neural network outputs to understand the decision-making process of the autonomous robot.
3 Feature Attribution
Using SHAP-like values and attention mechanisms, the system identifies which sensors and environmental factors most influenced the robot's decision.
4 Generate Explanation
A human-readable explanation is generated, showing confidence scores, decision pathways, and key influencing factors for complete transparency.
Pro Tips
- Modify sensor JSON to simulate edge cases (e.g., sensor failure, extreme weather)
- Compare different 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
- Export reports for documentation and compliance purposes
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 (CNN, RNN, Transformer), decision trees, random forests, and reinforcement learning policies. It uses feature attribution (like SHAP values, Integrated Gradients) and confidence scores from the model's output.
Is this a real-time explanation tool?
This demo processes static inputs for analysis. In a real robotic system, a similar pipeline would run in near real-time (10-100ms), logging decisions and their explanations for operator review, post-mission analysis, or live monitoring dashboards.
What are "decision pathways"?
Decision pathways are simplified visualizations of the data flow and inference steps: raw sensor input ā sensor fusion ā feature extraction ā model inference (neural network layers) ā action selection ā final command. This helps identify at which stage a particular factor influenced the outcome.
How are the key influencing factors calculated?
The explainer uses feature importance weights provided in the AI model output. In production systems, these come from techniques like Integrated Gradients, LIME, SHAP, or attention mechanisms in transformer-based architectures. The weights represent each feature's contribution to the final decision.
Why is AI transparency important in robotics?
In safety-critical domains like autonomous driving, healthcare, and industrial automation, operators and regulators need to trust AI decisions. Explainability provides transparency, helps verify that AI focuses on relevant factors, aids in debugging failures, and enables continuous improvement through human feedback.
Can I integrate this with my real robot?
Absolutely! The tool's logic can be adapted to receive real-time data from your robot's sensors and AI model via REST APIs or WebSocket connections. The explanation engine can run on your backend server or edge device, providing live decision transparency for your autonomous systems.