Dennis Hadjivelichkov

I am a PhD student at the CDT for Foundational AI at UCL, where I work on bridging the gap between robotics and intelligence through machine learning. I am part of the Robot Perception and Learning Lab where my PhD advisor is Dr. Dimitrios Kanoulas.

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I'm interested in robotics, computer vision and machine learning. Currently, my main focus is on improving self-supervised learning methods by and for robotics.

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Handling Joint Limits in Mobile Manipulator Whole Body Control via Reinforcement Learning

Denis Hadjivelichkov
MSc Dissertation, 2020

As part of my MSc Dissertation, improved the performance of a state-of-the-art method for mobile manipulator whole-body control by up to 24% in given custom environments. This was achieved by thorough investigation and simplification of the handcrafted reward signal used to optimize the system.

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Detection of carcinogenic weeds in aerial images using supervised learning

Denis Hadjivelichkov
Bachelor Thesis, 2019

Created a dataset containing carcinogenic weeds (Heliotropium Europaeum) in aerial imagery of farm fields. Applied and developed machine learning models for binary image classification. Developed an image processing algorithm for localization of weeds within the images. Dealt with a limited data and achieved a high recall rate.

Other Projects

These include coursework, side projects and unpublished research work.

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Autonomous Line-Following Buggy

University of Manchester

In 2017, I designed and developed an automous buggy as part of a four-member team. The buggy is able to follow a line and avoid obstacles such as walls, line gaps or slopes. The buggy implements a PID control system that corrects it’s errors in line estimation in real time. The sensors used are an array of 6x digital light sensors and 1x ultrasonic sensor. The software is embedded in a PIC microcontroller.

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Robust Mobile Face Recognition System

University of Manchester

I worked as a summer intern with Dr. Hujun Yin at the University of Manchester. I developed a mobile video face detection and recognition system on a Raspberry Pi. The system is controlled via speech. It uses Viola-Jones and Eigenface methods for detection and recognition. It is able to actively re-learn and add new faces.

Design and source code from Jon Barron's website