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Choose a Metric Hierarchy for a Humanoid-Robot Fleet

FigureaiData Engineer · Technical Screen
MediumData EngineerTechnical ScreenAnalytics & Experimentation

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This problem tests the ability to structure a multi-layered metrics dashboard for complex hardware-software systems, balancing operational efficiency against safety and system stability.

Problem Statement

You are the lead Data Engineer at a humanoid robotics company. We are preparing to deploy a new firmware update across our fleet of robots currently operating in warehouses. You need to define a hierarchical metric framework to monitor the health and performance of the fleet before, during, and after the rollout.

The framework must address the following dimensions:

  1. Task Completion: Efficiency of assigned goals.
  2. Autonomy: Ability to operate without human intervention.
  3. Reliability: Mean time between failure/intervention.
  4. Safety: Incidents, near-misses, or safety-stop triggers.
  5. Charging: Battery management and downtime.
  6. Data Health: Telemetry reliability and sensor stream consistency.

Requirements

  • Propose a top-level North Star metric and 3–5 supporting metrics for each of the dimensions above.
  • For each metric, define the numerator and denominator (e.g., 'rate per robot-hour' vs. 'rate per task').
  • Explain how you would account for exposure (e.g., comparing a canary rollout group to the rest of the fleet).
  • Address how you would handle 'noise'—specifically, how to distinguish between network-related outages and actual mechanical robot failure.

Hints

Hint 1

Consider that 'Reliability' should be normalized by 'Total Operational Time' rather than just a raw count of failures.

Hint 2

Think about trade-offs: a robot might achieve a perfect 'Task Completion' rate by being overly cautious, which might hurt 'Operations' or 'Battery Efficiency'.

Hint 3

Use a cohort-based approach (Canary vs. Control) to normalize for external environment variables like facility lighting or floor surface quality.

Follow-up questions

  • How would you detect if a specific hardware revision (e.g., a new batch of actuators) is performing differently than the rest of the fleet?
  • If the fleet is experiencing a sudden spike in 'Safety Stops', what logs and diagnostic data would you prioritize to isolate the root cause?
  • How would you define a 'Data Health' score to ensure that your analytical metrics aren't being skewed by corrupted telemetry packets from specific robots?