My name is Gregorio Orlando, and I am a fourth-year Computer Science and Artificial Intelligence student at IE University, currently based in Spain. I am also a Senior Research Assistant at the IE Robotics and AI Lab, where I lead two research projects—AIfred and Botzo—and support students working on robotics and AI initiatives.

I have always been passionate about programming, and especially about robotics, because it is where code becomes physical and interactive. Unlike traditional software development, where results are purely visual, robotics allows you to see your work come to life in the real world. This passion led me to join the IE Robotics and AI Lab early in my studies, where, under the mentorship of Prof. Eduardo Castelló, I rapidly developed hands-on engineering skills. I began with basic responsibilities such as maintaining 3D printers and progressively took on more complex and impactful projects. Today, my main focus is AIfred, a personal AI-powered study companion that will also serve as my capstone project, as well as Botzo, an open-source robotic dog platform designed for learning and research.

I am a computer scientist, I love to program, test code, and develop! But I am also used to taking notes, writing equations and solving problems on paper. We have so many tools online, but still many people and tasks are better in the physical world. AIfred bridges the digital and the physical in a natural way. In a physical environment you can get the benefits of both digital and physical world. Plus interact with digital content with physical actions such as gestures or moving objects.

One of the main challenges of the research was dealing with the complexity and uncertainty inherent in building a real-world human–robot collaboration system. Unlike structured assignments, research problems often have no clear solution path, and small changes in perception, hardware, or interaction design can significantly affect system behavior. Integrating multiple components—robot control, perception, AI reasoning, and user interaction—required constant iteration and debugging.

What helped me overcome these challenges was the problem-solving mindset developed throughout the program. Breaking complex problems into smaller components, testing assumptions, and iterating systematically allowed me to make steady progress even when systems initially failed. The strong technical foundations from my courses gave me the confidence to experiment, fail, and refine solutions, while the research environment taught me patience and resilience. Over time, these challenges became learning opportunities that significantly strengthened both my technical skills and my independence as a researcher.

This research experience allowed me to develop a strong and well-balanced set of technical and soft skills that are essential for a robotics engineer.

From a technical perspective, I significantly strengthened my ability to design, build, and integrate complex robotic systems. I gained hands-on experience working with robots using ROS, developing intelligent behaviors, and integrating perception, control, and AI components into a single coherent system. I also learned how to set up and manage complex development environments, efficiently install and adapt existing tools and frameworks, and leverage prior research and open-source solutions to accelerate development. In addition, the project helped me develop strong skills in system-level thinking, debugging, experimentation, and research-oriented development, including identifying meaningful research questions and contributing to scientific writing.

Equally important were the soft skills I developed throughout the process. Research taught me how to approach problems from multiple angles, remain persistent when solutions were not immediately clear, and manage uncertainty in open-ended challenges. I improved my ability to communicate ideas clearly, both when collaborating with other researchers and when documenting work for broader audiences. Working in a collaborative research environment also strengthened my teamwork, adaptability, and ability to give and receive constructive feedback.

Overall, this experience helped me grow from someone focused on individual technical tasks into a more complete engineer and researcher—capable of tackling complex problems, learning independently, and contributing meaningfully to long-term research goals.

How has this research experience influenced your academic path or career goals within Computer Science and AI?

Joining IE University and the IE Robotics and AI Lab was one of the best decisions of my academic journey. From the beginning, I became part of a welcoming and highly motivating environment that encourages curiosity, experimentation, and continuous growth. Long hours in the lab and challenging projects were never just about results—they were about learning, improving, and becoming better engineers every day.

Working under the mentorship of Prof. Eduardo Castelló played a crucial role in shaping my academic path. His guidance pushed me to go beyond my comfort zone, take ownership of complex systems, and approach problems with the mindset of a researcher and robotics engineer. Thanks to this mentorship, I was able to progressively build strong technical foundations and apply them to increasingly ambitious projects, including AIfred and external research collaborations.

This research experience helped me clearly define my passion for robotics and embodied AI, and it strongly influenced my career goals. I now aim to pursue a career centered on robotics research and development, where I can design intelligent systems that interact with the physical world and work closely with humans. The skills, tools, and problem-solving mindset I developed in the lab—combined with hands-on research experience—have given me confidence that my transition into advanced research or industry roles will be both natural and well-prepared.

Overall, the work done at the IE Robotics and AI Lab has provided me with a solid technical and professional foundation, making my future career path in Computer Science and AI clearer, more focused, and significantly more attainable.

Did participating in this research project help you better understand how your bachelor translates into real-world or applied research contexts? How?

Yes, very much so. Participating in this research project helped me clearly understand the strong connection between the theoretical foundations of my bachelor’s degree and their application in real-world research contexts. The relationship between coursework and lab work became bidirectional: what I learned in class directly supported my research in the Robotics and AI Lab, and the challenges I faced in the lab gave real meaning and context to what I was learning academically.

Concepts from my Computer Science and AI courses—such as algorithms, perception, machine learning, control systems, and software architecture—were constantly applied and tested through hands-on experimentation. At the same time, working on complex robotic systems like AIfred exposed gaps in my knowledge and helped me identify which topics I needed to explore more deeply in class and through independent study. This feedback loop made my learning more intentional and goal-driven.

As a result, I developed a clearer understanding not only of what I know, but also of what I still need to learn. This awareness has been crucial in shaping my academic interests and future career direction. The combination of coursework and applied research has given me the ability to approach problems with both theoretical rigor and practical awareness, preparing me to continue growing as a robotics researcher and engineer throughout my career.

My main advice is to show up and get involved. Go to the lab, express genuine interest, and be willing to get your hands dirty by taking on real challenges. Research at CyPhy Life is not about already knowing everything—it’s about showing curiosity, commitment, and a desire to go deeper than what you have learned in class.

Don’t be afraid to put yourself in difficult or unfamiliar situations, even if that means getting stuck for days while trying to solve a problem. Those moments are where the most learning happens. At CyPhy Life, initiative and perseverance matter far more than perfection.

Most importantly, Prof. Eduardo Castelló is always eager to support students who demonstrate motivation and a strong interest in the lab’s work. If you show that you are willing to learn, experiment, and push yourself, you will find a very supportive environment to grow both technically and professionally.