Artificial intelligence and robotics personal statement example

Why do you want to study this course or subject?

The thing that first held my attention was not a robot doing something clever, but a robot doing something badly. At a college open event a small wheeled robot kept nudging the same table leg, reversing, turning slightly, and nudging it again. I wanted to know what it thought it knew. That question has stayed with me: intelligence in a machine is not magic but a chain of assumptions about sensing, representing and deciding, and most failures happen where those assumptions meet an untidy room. Studying Mathematics and Computer Science together has shown me how much of this is structure rather than guesswork. Matrices stopped being an exercise once I used them to rotate coordinates for a simulated arm, and probability became interesting when I read about robots holding several possible positions at once and updating belief as sensor readings arrive. I am drawn to a degree that covers both the learning side and the physical side, because I want to work on systems that act in the world rather than only classify data about it. I am also interested in where these systems are deployed. Volunteering with children who use voice assistants daily has made me think about how confidently people trust an output they cannot inspect. A course combining artificial intelligence with robotics seems the right place to learn the mathematics and engineering properly while thinking carefully about reliability and use. Longer term I would like to work on assistive or agricultural robotics, where machines operate alongside people and imperfect conditions are the normal case, not the exception.

How have your qualifications and studies helped you to prepare?

My A levels in Mathematics, Physics and Computer Science have each contributed something distinct. Mathematics gave me the language: vectors, matrices, calculus and, in Further Mathematics topics I studied independently, an introduction to iterative numerical methods that made me appreciate why simulation steps matter. Physics has been the most useful for thinking about real hardware, particularly mechanics, moments and the behaviour of motors and circuits. Computer Science has developed my programming discipline; I now test small functions as I write them rather than building everything and hoping. For my coursework project I wrote a Python program that maps a small grid environment from simulated sensor data and plans a route using A* search, comparing it with a simpler greedy approach. The greedy version was faster but repeatedly trapped itself in dead ends, which taught me more about heuristics than reading about them had. My Extended Project Qualification examined how mobile robots estimate their own position, and I wrote about odometry drift and why combining wheel encoder data with other sensors improves estimates. Explaining Bayesian updating clearly to a non-specialist reader forced me to understand it rather than repeat it. Outside the syllabus I have read Melanie Mitchell's Artificial Intelligence: A Guide for Thinking Humans, which sharpened my scepticism about claims made for image classifiers, and I follow open course material on linear algebra to strengthen areas I find harder, especially eigenvectors. I have also been working through introductory material on control, because I realised my project assumed movement commands were executed perfectly.

What else have you done to prepare outside of education, and why are these experiences useful?

At home I share responsibility for my younger brother on weekday evenings while my mother works. I collect him from school, cook, and sit with him through homework. It requires planning the week in advance and holding to it, and it has made me realistic about time: I know how much study I can genuinely fit around fixed commitments, which is why I write down deadlines weeks ahead rather than trusting memory. On Saturday mornings I help at a coding club for primary school children at our local library. I support two or three children at a time with block-based programming, usually getting small robots to follow a drawn line. Debugging with an eight-year-old is a good exercise in breaking a problem into observable steps, and I have learnt to ask what they expected to happen before telling them what went wrong. It also showed me how quickly children accept that a machine simply knows things, which is partly what interests me about explaining how these systems actually decide. I work Sundays restocking at a garden centre, which pays for the parts I use on a second-hand Raspberry Pi robot kit. I have added an ultrasonic sensor and written code so it stops and turns before obstacles; the current problem is that soft objects like a curtain give unreliable readings, so I am experimenting with taking several measurements and discarding outliers. Progress is slow and involves a lot of loose wiring, but it is the most useful thing I do. Playing in a college five-a-side team each week keeps me from spending every evening at a desk, and I am the person who chases people for subs, which is less glamorous but equally necessary.

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