Start by clarifying requirements and constraints (e.g., warehouse environment, safety standards, robot speed). Then propose a layered architecture: perception (sensors + fusion), world modeling (data structures), and decision-making (control loop). Emphasize safety, real-time performance, and trade-offs between sensor cost, accuracy, and reliability.
Pro tip: Amazon values a safety-first, customer-obsessed mindset. Explicitly mention redundancy (e.g., multiple sensor modalities) and fail-safe behaviors (e.g., emergency stop) to show you prioritize human safety over task completion.
Ask about warehouse layout, human traffic patterns, robot speed, payload, and safety regulations. Define detection range, latency, and reliability requirements.
Select sensors (e.g., LiDAR, depth cameras, ultrasonic, radar) based on trade-offs. Describe a fusion approach (e.g., Kalman filter, sensor fusion) to combine data for robust human detection.
Represent the environment using occupancy grids, costmaps, or dynamic object lists. Use efficient data structures (e.g., KD-trees, voxel grids) for real-time updates and queries.
Create a control loop: sense → detect → predict → plan → act. Use algorithms like A* or RRT for path planning, and reactive methods (e.g., potential fields) for immediate avoidance. Include safety fallbacks.
Compare sensor costs, computational load, and failure modes. Explain how you would test (simulation, real-world) and iterate to ensure safety and efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They asked this near the end and I actually had a decent answer ready.
Start by clarifying the system's context and the depth camera's role, then outline a layered failure response: immediate detection, graceful degradation, and recovery. Emphasize safety, user experience, and how you would validate the solution through testing and monitoring.
Pro tip: Frame your answer around Amazon's leadership principles, especially 'Customer Obsession' and 'Ownership'—show that you anticipate failures and design for resilience, not just react to them.
Ask questions to understand the system's purpose, criticality, and how the depth camera contributes to core functionality. This shows you avoid assumptions and tailor your answer to the specific context.
Explain how you would detect a depth camera failure in real-time, such as through health checks, timeouts, or data validation. Mention the importance of low-latency detection to trigger fallback quickly.
Describe fallback strategies: switch to alternative sensors, use last known good data, or reduce functionality while maintaining safety. Highlight trade-offs between performance and reliability.
Outline steps to recover the camera or system, including retries, failover, and alerting. Discuss how to inform users or operators without causing panic, and log the event for analysis.
Explain how you would test failure scenarios (e.g., fault injection) and use monitoring to learn from incidents. Emphasize iterative improvement and post-mortem culture.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying what 'human-avoidance policy' means in context (e.g., a system that avoids humans for safety or efficiency). Then, describe an offline evaluation framework using historical data and counterfactual reasoning, focusing on metrics like precision/recall of human detection and impact on downstream outcomes. Emphasize the importance of defining success metrics and validating with a held-out dataset before any online A/B test.
Pro tip: Highlight the challenge of offline evaluation for policies that alter future states (e.g., avoiding humans changes the data distribution). Mention techniques like inverse propensity scoring (IPS) or off-policy evaluation to correct for distribution shift, showing depth beyond basic A/B testing.
Clearly specify what the human-avoidance policy does (e.g., a robot that steers away from humans) and what 'works' means: safety (no collisions), efficiency (time to goal), or comfort (minimal disruption). Choose quantifiable offline metrics like precision, recall, or cost.
Gather logs from past runs where the policy was not active (or a baseline policy was used), including sensor data, human positions, and outcomes. Ensure data covers diverse scenarios and is representative of deployment conditions.
Replay the historical data and apply the new policy to each state to generate counterfactual actions. Use a simulator if available, or model-based evaluation to estimate outcomes without real-world execution.
Compute metrics like expected reward under the new policy using importance sampling or doubly robust estimators. Compare against the baseline policy and check for statistical significance.
Test the evaluation on a holdout set or via cross-validation. If possible, run a small online A/B test to confirm offline findings, and refine the policy based on discrepancies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.