Case context: You are building an autonomous warehouse robot with a pipeline of three components: a pallet detector, a worker detector, and a route planner. On their own test sets, the pallet detector and worker detector both perform close to human-level. The route planner also performs close to human-level when it is given accurate aisle maps and obstacle locations. However, when the full system is tested using camera input, the robot often drives into blocked aisles and takes poor routes because it does not know where the aisle boundaries are.

Question: Based on this warehouse navigation pipeline, identify the main failure, explain why the end-to-end system performs poorly even though the individual components are strong, and choose one targeted pipeline change that would address the problem.

Sample answer: The main failure is missing information. The route planner is being asked to make good decisions without receiving essential aisle-boundary information. The pallet detector, worker detector, and route planner can all look strong when evaluated on their own inputs, but the full system still fails because the planner's inputs are incomplete. A good fix is to add an aisle-boundary detector that reads the camera image and passes aisle location information to the route planner. That gives the planner the missing context instead of forcing it to reason directly from raw camera frames.

Key points:

• Identify the problem as missing information, specifically aisle-boundary information.
• Explain that strong module performance does not help if the planner lacks the inputs needed for the real task.
• Propose adding an aisle-boundary detector to supply the route planner with the missing information.
• Avoid sending raw camera images directly to the route planner when a dedicated detector can provide the needed abstraction.

Rubric: The response must diagnose the issue as an information gap, explain that near-human performance of individual modules does not prevent overall failure when important inputs are absent, and recommend adding an aisle-boundary detector to feed the route planner rather than replacing the pipeline with raw image input.