Question 1
Difficulty: medium
Can you walk me through a robotics project you led from concept to deployment?
Sample answer
In my last role, I led the development of a mobile inspection robot for a warehouse environment. I started by working with operations and safety teams to define the real-world constraints: narrow aisles, reflective surfaces, variable lighting, and the need for reliable navigation around people and forklifts. From there, I translated those needs into technical requirements, selected the sensor stack, and built the initial ROS-based architecture. I handled the integration of LiDAR, wheel odometry, and camera data, then worked closely with software and mechanical engineers to iterate on the chassis and control logic. We tested in simulation first, then moved to staged field trials. The biggest challenge was reducing localization drift during long shifts, which we solved by tuning sensor fusion and adjusting map update logic. The robot eventually reached stable autonomous operation and cut manual inspection time significantly.
Question 2
Difficulty: hard
How do you approach sensor fusion when multiple sensors disagree with each other?
Sample answer
My first step is to understand why the disagreement is happening rather than forcing a quick software fix. I look at timing, calibration, noise characteristics, and whether one sensor is failing under certain environmental conditions. In practice, I usually start by checking timestamps and frame alignment, because a lot of apparent disagreement comes from latency or poor synchronization. After that, I compare sensor confidence and the failure modes of each device. For example, a camera may struggle in low light while a LiDAR can be affected by reflective surfaces. I then design the fusion strategy around those strengths and weaknesses, often using a weighted filter or state estimator that can adapt based on confidence. I also build diagnostics into the system so we can see when one sensor starts drifting or dropping quality. That makes the robot more robust in the field and much easier to maintain.
Question 3
Difficulty: medium
Describe a time when a robot system failed in testing. What did you do?
Sample answer
During one test cycle, a robot arm started missing pick positions intermittently after running well for several minutes. Instead of assuming it was a single issue, I treated it like a system-level fault and broke the problem down. I reviewed logs from the controller, checked mechanical backlash, and looked at temperature changes over time. It turned out the main cause was a combination of belt tension loosening slightly under load and a calibration routine that was too optimistic after startup. I worked with the mechanical team to improve the tensioning design, and I updated the software to require a quick verification step before full-speed operation. I also added a test case that replicated the failure faster so we could validate the fix. I think the key was staying calm, using data instead of guesswork, and treating the failure as a chance to improve the design rather than just patching the symptom.
Question 4
Difficulty: hard
What steps do you take to ensure a robot is safe to operate around people?
Sample answer
Safety is one of the first things I think about, not something I add later. I start by identifying the likely hazards: collision risk, pinch points, unexpected motion, runaway states, and sensor blind spots. From there, I build layered protections. On the hardware side, I make sure emergency stop behavior is reliable and easy to access, and I verify that torque or speed limits are appropriate for the environment. On the software side, I add state checks, watchdogs, and fail-safe modes so the robot can stop cleanly if a critical subsystem behaves unexpectedly. I also pay attention to perception, because a robot is only as safe as its understanding of the space around it. In testing, I run scenarios with people intentionally crossing its path to verify reaction times and stopping distances. Just as important, I work with operators to make sure the system is easy to understand, because clear human interaction reduces misuse and surprises.
Question 5
Difficulty: medium
How do you decide between using simulation and physical testing in robotics development?
Sample answer
I use simulation and physical testing for different reasons, and I try not to over-rely on either one. Simulation is excellent early in the process because it lets us test control logic, navigation strategies, and failure scenarios quickly without risking hardware damage. It’s especially useful for comparing design options and uncovering obvious flaws before we build anything expensive. That said, simulation never captures every real-world detail, so I use it as a filter, not a final proof. Once the concept looks promising, I move to controlled physical tests to measure things like sensor noise, wear, latency, and unexpected environmental behavior. I also like to validate assumptions in stages, starting with bench tests, then lab tests, then field trials. That sequence gives me confidence without wasting time. In my experience, the best results come from combining both methods and being honest about the gaps between them.
Question 6
Difficulty: medium
Tell me about a time you had to work with electrical, mechanical, and software teams on one robot system.
Sample answer
In one project, I worked on a warehouse robot that required tight coordination across all three disciplines. The mechanical team was focused on keeping the base compact and durable, electrical needed room for power distribution and sensors, and software wanted clean access to data with minimal latency. Early on, those priorities were pulling in different directions, so I created a shared requirements document and a weekly integration review to keep everyone aligned. We used interface definitions for connectors, mounting points, and communication protocols so changes in one area wouldn’t create surprises elsewhere. I also made sure to translate technical tradeoffs into language each team cared about. For example, when we changed a sensor mount to improve field of view, I explained the impact on calibration and cable routing, not just the geometry. That cross-functional approach reduced friction and helped us catch integration issues before they became expensive rework.
Question 7
Difficulty: hard
How do you debug motion planning or path planning problems in a robot?
Sample answer
I start by separating the problem into perception, planning, and actuation. If a robot is taking poor paths, I first check whether the map or environment model is accurate, because a bad input can make the planner look broken when it isn’t. Then I review planner parameters such as obstacle inflation, goal tolerance, and cost weights to see whether the system is being too conservative or too aggressive. I also look at the controller output to confirm the path is actually feasible for the robot’s dynamics. Sometimes the planner generates a path that is valid geometrically but unrealistic for acceleration, turning radius, or wheel slip. In those cases, I tune the planner and the controller together rather than in isolation. I rely heavily on logging and replay tools, because being able to reproduce the exact conditions is critical. The goal is to find whether the issue is algorithmic, environmental, or mechanical before making changes.
Question 8
Difficulty: easy
What experience do you have with ROS or other robotics software frameworks?
Sample answer
I’ve used ROS extensively for building modular robotics systems, especially where I needed clean communication between navigation, perception, and control components. What I like about ROS is that it supports a structured workflow for launching nodes, managing topics and services, and keeping the system easier to debug as it grows. In one project, I used ROS to integrate LiDAR-based localization, obstacle avoidance, and teleoperation fallback modes. I also spent time tuning message rates and reviewing how nodes interacted under load, because a robotics system can look fine in small tests and then become unstable when latency increases. Beyond basic use, I’ve worked on building reusable packages and improving launch reliability so testing is more repeatable. I’m comfortable with the idea that frameworks are tools, not magic; the real value comes from designing clean interfaces and understanding the robot’s behavior end to end.
Question 9
Difficulty: medium
How do you handle tradeoffs between accuracy, cost, and reliability in robot design?
Sample answer
I approach that tradeoff by first understanding what level of performance is actually required for the use case. Not every robot needs the most expensive sensor suite or the highest possible precision. If a system is doing routine material transport, for example, reliability and maintainability may matter more than extreme accuracy. I try to define acceptable error ranges, uptime goals, and maintenance expectations before making design choices. Then I evaluate options against those requirements instead of chasing technical performance for its own sake. I’ve found that the most elegant solution on paper is not always the best choice in production, especially if it’s hard to service or too sensitive to environmental variation. I also like to use prototype data to support the decision rather than relying on intuition. That way, the final design is grounded in actual operational needs and not just engineering preference.
Question 10
Difficulty: easy
Where do you see robotics technology creating the most value in the next few years?
Sample answer
I think the biggest value will come from robots that can operate more safely and reliably in messy, real-world environments rather than just controlled factory settings. There’s already a lot of progress in warehouse automation and manufacturing, but I’m especially interested in applications like inspection, logistics, agriculture, and assisted labor where the environment changes constantly. The real opportunity is not just better hardware, but better autonomy, better perception, and smoother human-robot collaboration. As models improve and sensors become more affordable, robots will be able to take on more repetitive and physically demanding work while people focus on judgment-based tasks. I also think there’s growing value in designing systems that are easier to deploy and maintain, because adoption often fails when the technology is too complex for end users. The companies that succeed will likely be the ones that make robotics practical, not just impressive.
