Boston Dynamics Robot Algorithm Interview: Motion Control, Path Planning, and Human-Robot Interaction

Background

Let me start with my background. I have a master's degree and two years of experience working at UBTECH as a robot algorithm engineer, mainly focusing on motion control and path planning. Honestly, I applied for this position on a friend's recommendation. They told me the robot algorithm role at Boston Dynamics was challenging and the compensation was decent, so I decided to give it a shot. Going through the entire interview process really gave me a completely new perspective on robot algorithms.

At UBTECH, I primarily worked on gait control for humanoid robots, with some involvement in path planning and obstacle avoidance algorithms. I used C++ and Python extensively in my daily work, and ROS was a constant companion. Before the interview, I was actually pretty confident — I thought my project experience would be enough. But after the first round, I realized my fundamentals weren't as solid as I thought. So I'm writing this article to document my interview experience and hopefully help others preparing for robot algorithm interviews.

Interview Process Review

Round 1: Kinematics + Dynamics (Technical, ~90 minutes)

The first round had two interviewers — one focused on kinematics and the other on dynamics. They started with a self-introduction and then jumped straight into questions at a very fast pace.

The first question was about the difference between forward kinematics and inverse kinematics. I answered this reasonably well — forward kinematics computes the end-effector pose from joint angles, and inverse kinematics does the reverse. But the interviewer immediately followed up with a question that caught me off guard: What are the conditions for the existence of a closed-form solution for inverse kinematics of a 6-DOF manipulator? I could only recall the Pieper criterion, stating that a closed-form solution exists when three consecutive joint axes intersect at a point. The interviewer nodded and then asked about the convergence issues of the Newton-Raphson method in numerical inverse kinematics. I didn't answer this well — I only mentioned that convergence fails when the Jacobian is singular. The interviewer added that initial value selection is also critical.

Next was the dynamics section. The interviewer asked about the pros and cons comparison between Lagrangian dynamics and the Newton-Euler method. I said the Lagrangian method has a unified formulation but is computationally expensive, while the Newton-Euler method is efficient through recursion but complex to derive. Then the interviewer asked me to derive the dynamics equation for a two-link manipulator by hand. This took me about ten minutes, and I made a sign error that the interviewer pointed out — a bit embarrassing.

The last question was about the difference between impedance control and admittance control. I answered this more completely, discussing the difference between force-controlled and position-controlled approaches and their applications in compliant control. The interviewer seemed satisfied.

Round 2: Path Planning + SLAM (Technical, ~100 minutes)

The second round interviewers were more senior, and the questions were deeper. They started by discussing my previous path planning projects, then moved to theoretical questions.

The first question was about the difference between A* and Dijkstra's algorithm. This was fairly basic — I explained the role of the heuristic function. But the interviewer immediately followed up with the admissibility condition of the heuristic function and when A* is not optimal. I said that if the heuristic function is not admissible (i.e., overestimates the actual cost), A* may not find the optimal path. The interviewer then asked about the principle and use cases of weighted A*. I explained that it accelerates search by amplifying the heuristic function, suitable for scenarios with high real-time requirements.

The SLAM section was quite in-depth. The interviewer asked about the comparison between feature-based methods and direct methods in visual SLAM. I answered that feature-based methods are robust to illumination but sparse, while direct methods are dense but sensitive to lighting changes. Then I was asked about the system architecture of ORB-SLAM2. I described the three modules: tracking, local mapping, and loop closing. The interviewer followed up on the principle of DBoW2 in loop closing. I didn't answer this in enough detail — I only mentioned the basic idea of bag-of-words. The interviewer supplemented with hierarchical clustering and inverted indexing.

The final question was open-ended: How do you do path planning in dynamic environments? I answered from several angles — dynamic obstacle prediction, space-time A*, and velocity obstacles. The interviewer thought the approach was reasonable but said engineering implementation requires considering more factors.

Round 3: Human-Robot Interaction + Project Deep Dive (Comprehensive, ~80 minutes)

The third round was with the department head. They first asked me to describe my most satisfying project in detail. I talked about the robot gesture interaction system I had built, from design to algorithm selection to final results. The interviewer asked many detailed questions, such as how I optimized the latency of gesture recognition, the specific strategy for multimodal fusion, and how user experience was evaluated. These questions made me realize that algorithm development isn't just about metrics — user experience matters equally.

The interviewer then asked an interesting question: If you were to design an interaction system for a home service robot, how would you approach it? I answered from the dimensions of voice interaction, gesture recognition, affective computing, and safety mechanisms. The interviewer said the approach was good but reminded me to pay special attention to privacy and safety, which are critical in home scenarios.

We ended with a discussion about career planning. I said I wanted to move toward embodied intelligence, and the interviewer acknowledged this, saying it's indeed a promising direction.

Key Interview Questions

1. Difference between forward and inverse kinematics? Conditions for closed-form inverse kinematics of a 6-DOF manipulator?

2. Convergence issues of Newton-Raphson method in numerical inverse kinematics?

3. Pros and cons comparison between Lagrangian dynamics and Newton-Euler method?

4. Derive the dynamics equation for a two-link manipulator by hand

5. Difference between impedance control and admittance control? Application scenarios?

6. Difference between A* and Dijkstra? Admissibility condition of the heuristic function?

7. Principle and use cases of weighted A*?

8. Comparison between feature-based and direct methods in visual SLAM?

9. System architecture of ORB-SLAM2? Principle of DBoW2 in loop closing?

10. Path planning methods in dynamic environments?

11. Latency optimization for gesture recognition systems?

12. Multimodal fusion strategies?

13. Designing an interaction system for home service robots?

Insights and Advice

1. Fundamentals must be solid. Many interview questions seem basic, but interviewers will keep pushing deeper. For example, inverse kinematics went from concepts to closed-form solution conditions to numerical methods, layer by layer. If your fundamentals aren't strong, it's easy to expose weaknesses under follow-up questions.

2. Be able to explain project details clearly. In the third round, the interviewer asked very detailed questions about my project — not just what algorithms I used, but why I chose them, what problems I encountered, how I solved them, and what the results were. I recommend thoroughly reviewing your projects before the interview.

3. Stay current with cutting-edge directions. The robotics field evolves rapidly. Interviewers will assess your knowledge of emerging technologies like embodied intelligence and LLM-driven robot control. It's best to have some understanding of these areas.

4. Value engineering skills. Algorithm interviews aren't just about deriving formulas — engineering implementation matters equally. I was repeatedly asked about practical engineering issues like ensuring real-time performance and system deployment.

5. Maintain a good mindset. It's normal to encounter questions you can't answer. Don't panic. Being able to articulate your thought process and demonstrate analytical skills is more valuable than reciting answers.

FAQ

Q: What programming languages should I prepare for robot algorithm interviews?

A: C++ and Python are essential. For C++, familiarity up to C++17 is recommended. For Python, you should at least be proficient with NumPy and SciPy. If you're in SLAM, you also need to know ROS/ROS2.

Q: Are there many hand-derivation questions in the interview?

A: There's a fair proportion, but it's not a pure math exam. Interviewers care more about your understanding of formulas than rote memorization. I recommend deriving common kinematics and dynamics formulas yourself — understanding the derivation process is more important than memorizing the results.

Q: Can I apply without robotics experience?

A: Yes, but you need relevant foundational knowledge. For control, you should understand modern control theory; for planning, graph search algorithms; for perception, computer vision. Interviewers will adjust question difficulty based on your background.

Q: What's the work intensity like at Boston Dynamics?

A: From what I learned during the interview, there's more overtime when projects are tight, but it's manageable otherwise. Robot algorithm work inherently requires extensive experimentation and debugging, so the pace is indeed faster than pure software roles.