Embodied AI Interview Preparation and Review Template
This template is used to organize project experiences into verifiable technical responses, and also to record questions, evidence, and improvements after an interview. It is recommended to copy it into personal notes for filling in, rather than writing down names, contact information, company internal details, or undisclosed data directly in public materials.
1. Before the interview: Project description card
For each key project, only one description card should be filled out, and it should be within one page.
| Item Elements | Fill-in Content |
|---|---|
| Task Objective | What task the robot should complete in what environment |
| Observation Inputs | Images, depth, point clouds, joint states, language commands, etc. |
| Action Outputs | Joint positions, end pose pose, velocity, torque, or discrete actions |
| Data Sources | Data scale, collection method, split of training and evaluation sets |
| Model and Algorithm | Policy structure, loss function, planner or controller |
| Training Configuration | Hardware, number of training steps, batch size, learning rate, and time consumption |
| Evaluation Protocol | Number of tasks, number of iterations per task, success criteria, and fixed seeds |
| Final Results | Success rate, latency, throughput, memory usage, and typical failure stages |
| Personal Work | Modules completed independently, issues fixed, and submission records |
Project introduction can be organized in the format of “task—constraint—method—result—review”:
- Task: A single sentence describing what the robot should do.
- Constraints: Describing limitations in hardware, data, time, or deployment environment.
- Method: Only discussing design choices directly related to the results.
- Results: Providing numerical values and observable phenomena under a fixed evaluation protocol.
- Review: Explaining the main failure modes and the most valuable improvements for the next step.
2. Technical Issue Records
Problems encountered during the interview should be recorded in the table below. For questions that were not answered, write down the reasoning process at that time, so as to determine whether the issue lies with concepts, formulas, coding experience, or experimental experience.
| Question | Response at that time | Evidence or formula | Gap in response | Revised answer |
|---|---|---|---|---|
| Example: Why does behavior cloning cause distribution shifts? | ||||
| Example: How to define a truly successful grasping? | ||||
| Example: How to align images, states, and actions in time? |
3. Experiment Question Checklist
When answering questions about training, deployment, or evaluation, prioritize providing the following information:
- Tensor shape, coordinate system, action units, and control frequency;
- Dataset round boundaries, timestamps, normalized statistics, and abnormal trajectory processing;
- Whether training and inference use the same observations and actions definitions;
- Whether physical stages such as contact, lifting, transportation, placement, and release are checked for success determination;
- Random seeds, number of tasks, number of rounds per task, and result aggregation methods;
- Model latency, control cycle, memory usage, and failure recovery policies.
4. Post-interview Review
Complete the three tasks as soon as possible after the interview ends:
- Classify the new problem into categories such as robotics, perception, planning, control, learning, or engineering deployment.
- Provide textbook sections, papers, source code, or experimental records for the key conclusions, and avoid retaining vague expressions based on memory alone.
- Choose the weakest problem, write minimal verification code or manual calculation process, and record the expected output.
The standard for completing the review is not to “understand the answer,” but to be able to explain the input, processing, output, assumptions, and validation methods without looking at the original text.