Your starting point
Start with a subsystem, not with the ambition to build an entire robot. If your strongest work is reliable software, a sensible first target is the boundary between sensor inputs, state estimates and application behavior. Keep research-heavy perception roles separate from integration roles when reading vacancies: both can contain “robotics software” in their title.
What you can carry over
- Testing and observability
- Apply these to recorded sensor streams, state changes and reproducible failures.
- Systems and performance work
- Investigate latency and resource budgets instead of assuming a fast desktop result will hold on a robot.
- API and service design
- Make message contracts, units, timestamps and failure states explicit.
Skills to build next
Work through coordinate frames, transformations and the difference between a planned path and a controller following it. Learn enough state estimation to explain where a pose estimate comes from and when it becomes unreliable. The goal is not to claim expertise in every robotics algorithm; it is to trace one behavior through the system and diagnose a failure without treating the robot as a black box.
Portfolio project: Build a repeatable navigation evaluation
This is a suggested project, not a required credential or a promise of employability.
- Start with a supported simulated mobile-robot example. Pin the software versions and save a small set of routes, obstacles and initial conditions.
- Run a baseline, then vary one condition such as obstacle placement or sensor delay. Record completion, collisions, elapsed time and the reason for each failure.
- Choose one failure to investigate. Trace it through sensor input, localization, planning and control; change one component or configuration and rerun the same cases.
- Publish the run instructions, result files and a short analysis. Separate your changes from upstream tutorial code and show at least one case your changes do not solve.
Keep the evidence honest: Simulation is a valid starting point. Label it clearly; do not describe simulator success as validated performance on physical hardware.
What to put in the evidence package
Your strongest application artifact is a small repository with a reproducible experiment and an explanation of the tradeoff you made. A reviewer should be able to find the baseline, the change, the measured result and the limitation. On your résumé, distinguish your implementation from the framework you integrated; a tutorial launch alone does not demonstrate ownership of navigation software.
Turn the project into a focused application
Read several related vacancies and separate requirements into skills you can demonstrate now, gaps your project can address and experience that requires a different role first. Apply where that evidence fits the actual responsibilities. A senior title or a research publication requirement is not removed by completing this learning path.
Questions to ask the employer
- Does this role own algorithms, integration or deployment reliability?
- Which language, middleware and runtime constraints matter for this team?
- How are regressions reproduced, and is regular lab attendance required?
Learning references
Start with the relevant section of these primary resources. They support the technical learning topics; the project plan and career advice above are our editorial suggestions, not employer endorsements.
- Nav2 documentation — Use the navigation concepts, setup and tuning sections to understand how a mobile-robot stack fits together.
- Modern Robotics course resources — The rigid-body motion, planning and wheeled-robot material provides a mathematical reference.