Over the past few years, I have found myself constantly asking one question: where do all my interests truly meet?
My journey has taken me through electrical and electronic engineering, with a focus on control systems, into artificial intelligence, and then into cloud computing. For a while, these felt like separate paths I was exploring one after the other. But with time, a pattern started to emerge. There was one field that quietly sat at the center of all of them—robotics.
It made sense the more I thought about it. Robotics brings together control systems, especially the nonlinear aspects I have always been interested in. It creates space for artificial intelligence to play a real role in perception and decision-making. And with the direction technology is heading, cloud computing becomes an added advantage for scaling and coordination. It wasn’t just another field; it felt like a natural convergence point.
After spending some time working in the automation and telecommunications industries, I returned to research about few years ago. My current doctoral work focuses on designing control systems for coordinating multi-agent systems, particularly mobile robots operating within a shared environment using model predictive control. That experience, combined with several robotics courses I took along the way, opened my eyes to both the beauty and the difficulty of the field.
I began to appreciate the depth of mathematics behind control systems, but also the gap that often exists between theory and real-world implementation. I could follow tutorials, understand parts of the code, and even replicate certain results using ROS-based mobile robot platforms. With the help of available resources and even AI tools, I managed to get some experimental setups running. But deep down, I knew something was missing.
There were moments I struggled, not because I wasn’t trying, but because I didn’t fully understand some of the fundamentals. I often felt like I was building on top of incomplete knowledge, and that made me question whether there were better, more structured ways to approach what I was doing.
At the same time, the field itself is evolving quickly. Tools like ROS2 are becoming essential, and strong programming skills in languages like Python and C++ are no longer optional. It became clear to me that if I wanted to grow properly in robotics, I needed to go back and strengthen my foundation, this time with more structure and intention.
That was when I came across Aurora Robotics 2.0. It happened almost by chance, just a day or two before the training was about to begin. I didn’t overthink it. I signed up immediately. And alongside that decision, I made another one, to document the journey as it unfolds.
The first day turned out to be quite different from what I expected. There were no robots involved. No lines of code written. Yet, it was one of the most important sessions I have had in a while.
The orientation introduced us to the structure of the program and the people behind it, including Bakel Bakel and the rest of the team. Listening to them and reflecting on their journeys was surprisingly grounding. It reminded me that learning is not always about how far you have gone, but about how well you understand what you are doing.
At some point during the session, I found myself reflecting on a simple idea that stayed with me:
Knowledge is not about age or level of education. It’s about whether you know it or you don’t.
It felt obvious, almost too simple, but also very honest. A reminder to focus less on appearances and more on actual understanding.
We were also introduced to the way the training is structured, moving from concepts and mathematics into code and simulation, and eventually into real-world application. That progression immediately resonated with me because it directly addresses the gap I have been experiencing in my own learning.
Beyond the technical direction, there was also a strong emphasis on mindset. Curiosity was encouraged over perfection. We were reminded that failure is part of the process, not something to avoid. The idea of failing fast and learning faster stood out, as well as the importance of consistency over short bursts of intense effort. There was also a clear message about collaboration, that robotics is not something you do alone.
By the end of the session, I realized that what we had gained was not technical knowledge, but something just as important—a reset in how to approach the journey ahead.
We also briefly touched on the tools we will be using, which will be explored more deeply in the next session. That will likely be the point where things start becoming more hands-on.
For now, this feels like the right starting point. Not with code or hardware, but with clarity.
This is Day 1 of my robotics journey with Aurora Robotics.
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