For my practicum, I was working as a research intern at the University of Minnesota under the supervision of Professor Stephen J. Guy in the Applied Motion Lab. The goal of the project was to formulate a model that would allow a robot to autonomously be able to accompany a person. The project was motivated by a collaboration between the lab and the forestry department when they requested to borrow the quadruped from the lab. A typical day in the lab starts with me working on my own on the tasks that the graduate student, directing me and Professor Guy decided on the day before. The tasks ranged from debugging the simulator code I was using to trying to set up the robot to run any experiments that would happen later in the day, to experimenting with different types of models that I was training to achieve better accuracy. In the afternoon, the graduate student would come in, and we would discuss any progress I made in the morning and sketch out a plan for tasks I should accomplish before the end of the day. If the professor were in the lab on that day, there would be a more detailed discussion of my results and reevaluating or redirecting the trajectory of the project. By observing other people around the lab, I noticed that a lot of the research problems revolved around multidisciplinary collaboration and the sharing of knowledge. To find the best solutions, people with the specific domain knowledge needed to be consulted for research projects, and required intensive discussions and deliberations. This reflected the theoretical concepts that I learned from the SGC colloquium, where scientific discoveries are not made by singular persons, but rather require a wide-scale collaboration between people of different disciplines. Only through the free exchange of ideas between different areas can scientific discoveries come into light. An example of this is my observation of another project that was ongoing in the same lab where I was working. The project was based on motion-tracking, where the researchers were using software and algorithms to analyze the movements of prematurely born babies to determine whether they would develop cerebral palsy in the future. It required collaboration between the kinesiology department and the computer science department. This enriched my perspective and unlocked another dimension of how research is conducted. Through this experience, I learned more about what interested me and what potential future careers I would be interested in. I learned that I can learn more quickly than I thought, learning the different technologies and concepts that were required to conduct the research. Based on the work that I had done during the research internship, I realized that work that required more thinking was more fulfilling and rewarding than creating with tools that were already well-made, such as web development or software engineering. I had the opportunity to develop the skills of scientific communication and asking good questions. In order to figure out what information that I needed to know, I had to be well-tuned to the knowledge that I was lacking and practice formulating questions that would allow the people around me to help me and fill in any gaps in my thinking. I also practiced the skill of time management and knowing when to persist in a problem and when to ask for help, since, through the internship, I realized that staying stuck on a problem and trying to figure out how to solve the issue on my own was counterproductive and prevented me from progressing in the project. If I could have better prepared for this research opportunity, I would have taken more courses in linear algebra, machine learning, and robotics so that instead of having to learn that knowledge from scratch for the first couple of weeks, I could have made more progress in the project. After the experience, I took a graduate course in machine learning and modeling, using the research project as my final course project to incorporate more techniques to solve the problem. This semester, I am taking a course in robotics to learn the fundamentals of the field so that I can gain the necessary background knowledge to progress in the project in a meaningful way, and hopefully result in the project being published at a conference. After graduation, I’m thinking of doing a master's degree or a PhD in robotics to continue doing research in social robotics. During my research internship, I was largely working with a graduate student and my professor. My role was mainly as a mentee, doing more of the “grunt work” of data analysis, model training, and building simulators to validate the behavior of the model, while the graduate student and my professor were the guiding forces behind the project. They came up with different ways to approach the problem and suggested things that I should try. It was familiar in a way that I was in a position to absorb the knowledge that they were giving me, but also new in the way that, unlike my previous experiences, they allowed me to be an active participant in the discussion by asking me what I didn’t know and explaining from scratch the ideas that they were coming up with. I would recommend this opportunity to future SGC students because it enriched my perspective on how research works and what the process looks like. At the very worst, it could confirm that one doesn’t like doing research, but even then, it could eliminate a potential career path and narrow down what truly interests them. I got the chance to not only work with a professor one-on-one, but I also met so many talented people from other universities as part of my cohort in the program, and interacted with them on a personal level. The connections and ideas that I gained from the program were invaluable and something that I would treasure.