Explainability Layer

Highlights the specific regions of input data—such as LiDAR point clouds or camera pixels—that most influenced an agent's decision. Saliency maps visually overlay heatmaps onto sensor feeds, allowing an operator to instantly see why a robot chose a particular path. For example, a map might reveal that a plastic bag, not a solid obstacle, triggered an emergency stop, preventing unnecessary manual overrides.

Converts the agent's internal state vectors and cost-function outputs into a concise, human-readable text explanation. Using a large language model as a translation layer, the system can articulate reasoning such as: "I am rerouting to Aisle 7 because Aisle 5 is blocked by a detected forklift, and this alternative path minimizes battery consumption by 15%." This bridges the gap between numeric optimization and operator comprehension.

Presents 'what-if' scenarios to clarify decision boundaries. The layer answers questions like: "Why did the agent turn left instead of right?" by showing that a right turn would have resulted in a deadlock with Agent-42 in 3.2 seconds. This technique explicitly contrasts the chosen action against rejected alternatives, building operator trust in the fleet's spatial-temporal scheduling logic.

Distills a complex neural network policy into a simplified, interpretable surrogate model—often a shallow decision tree. While not perfectly accurate, this approximation reveals the dominant logical rules governing an agent's behavior. An operator can inspect a rule like: "IF battery < 20% AND distance to charger < 50m THEN return to dock," providing a high-level mental model of the agent's priorities.

Identifies the precise moment in a sequence of past events that caused a current state. For a collision-avoidance maneuver, temporal attribution might pinpoint that a velocity change 1.5 seconds prior, triggered by a misclassified pedestrian, was the root cause. This is critical for post-incident analysis and debugging, linking an outcome to a specific frame in a sensor stream.

Breaks down the agent's overall decision confidence into its constituent parts: perception uncertainty (e.g., 92% sure an object is a pallet) and prediction uncertainty (e.g., 60% sure the pallet will remain static). This decomposition allows an operator to quickly assess whether a low-confidence action stems from noisy sensor data or an unpredictable environment, guiding the appropriate intervention strategy.