Tahir Hasan

From mechatronics to computer vision: what transfers

· 1 min read · career robotics

Work in progress. This is a draft outline — the full post is being written.

I came to computer vision from a mechatronics degree — DH parameters and PID loops, not transformers. The retraining was real, but less of it was wasted than I expected: most of what an engineering education actually teaches is how to debug a system you don’t fully understand, and that skill transfers to machine learning embarrassingly well.

Outline of the full post:

  1. Kinematics is just geometry — and geometry is half of classical vision (camera models, homographies, my Modified-DH habit of writing frames down explicitly).
  2. Control loops → training loops: feedback, stability, and why “the loss went down” is the same trap as “the step response looks fine”.
  3. Hardware debugging discipline: change one thing, log everything, distrust the sensor first — i.e., distrust the dataloader first.
  4. What didn’t transfer: the statistics gap, and how I closed it.
  5. Advice for engineers crossing over (what I’d tell 2022-me).