I am director of the Robot Learning Lab at Imperial College. I received a BA and MEng in Electrical and Information Engineering from Cambridge University, and a PhD in vision-based robot localisation from Imperial College, working with Guang-Zhong Yang in the Hamlyn Centre. Following my PhD, I spent a year as a postdoc at UCL working with Gabriel Brostow, and I then returned to Imperial College as a founding member of the Dyson Robotics Lab with Andrew Davison, where I led the robot manipulation team. In 2017, I was awarded a Royal Academy of Engineering Research Fellowship for my project "Empowering Next-Generation Robots with Dexterous Manipulation: Deep Learning via Simulation", and then in 2018 I was appointed as a Lecturer at Imperial College, and founded the Robot Learning Lab. Alongside leading the lab's research, I teach a graduate-level course on Reinforcement Learning.

Before beginning my PhD, I completed a Bachelor’s degree in Physics and a Master’s degree in Computing (Machine Learning) at Imperial College London. During my Master’s degree I completed my individual project under the supervision of Dr. Edward Johns. The focus of this project was on learning stochastic policies from demonstrations and a sparse reward function. My PhD will extend this project and will investigate methods for robot learning of contact-rich task from demonstrations and a sparse reward function, which are both safe and efficient, and are suitable for deployment in an industrial setting.

I received my Bachelor's degree in Physics and Structural Engineering at Bogazici University, Istanbul, Turkey, in 2015. Following that, I completed my Master's degree developing system identification and damage detection algorithms at MIT. I specifically worked on Bayesian model updating methods and explored deep learning for earthquake characterization. Before joining the Robot Learning Lab as a PhD student, I led the research efforts in a computer vision startup in Berlin, which focuses on re-engineering the way humans interact with computers. I am currently working on robot manipulation and long-horizon planning for complex tasks. 

I am a PhD student at Imperial College London working at the Dyson Robotics Lab, where I am co-supervised by Dr. Edward Johns and Prof. Andrew Davison. I completed my MRes with Distinction at the same lab working on multi-task and auxiliary learning. Prior to joining Imperial College, I obtained my BS with Honours in Mathematics and Electrical Engineering at the Penn State University. I have also interned at The Robotics Institute at Carnegie Mellon University, Tencent - YouTu Lab, and Adobe Research. My general research interest is to build learning frameworks which can induce learning algorithms automatically, with no or minimal human supervision. This includes learning a universal representation from various tasks; automating network architecture design with adaptation to different input signals; and showing a quick mastery of new tasks based on previous experiences.

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Publications

ECCV 2020 Shape Adaptor: A Learnable Resizing Module

NeurIPS 2019 Self-Supervised Generalisation with Meta Auxiliary Learning

CVPR 2019 End-to-End Multi-Task Learning with Attention

I received a BSc. in Control Engineering from University of Naples Federico II and an MSc. in AI & Robotics from Sapienza University of Rome. Over the years, I had the opportunity to work and conduct research at several institutes, startups and universities around the world. In the summer of 2017 I visited Tohoku University (Sendai, Japan), working on wheeled robots for space exploration. In the summer of 2018 I’ve been an AI research intern at Curious AI (Helsinki, Finland), where I developed state-of-the-art techniques in model-based reinforcement learning. In the summer of 2019 I conducted research at the Italian Institute of Technology (Genoa, Italy), working on quadruped robots. I joined the Robot Learning Lab first as a visiting researcher during the winter of 2019, and was then accepted for a PhD the following year. I research and design methods that allow robots and humans to collaborate in novel, intuitive and effective ways.

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Publications

IROS 2021 Coarse-to-Fine for Sim-to-Real: Sub-Millimetre Precision Across Wide Task Spaces

I am a PhD student at Imperial College London, working in the Robot Learning Lab. Prior to joining Imperial College, I received a Bachelor’s degree in Applied Physics from Vilnius University, Lithuania. Following my studies, I also carried out research with a focus on laser beam shaping. Before becoming a PhD student, I completed an MSc in Artificial Intelligence at Imperial College London. I completed my MSc individual project under the supervision of Dr. Edward Johns. During the project, I investigated ways of using Deep Learning to increase the efficiency of planning algorithms, focusing on robot motion planning. My current research interests are robot manipulation, hierarchical planning and efficient replanning for completing complex tasks.