I am Director of the Robot Learning Lab at Imperial College London, where I am also a Senior Lecturer and a Royal Academy of Engineering (RAEng) Research Fellow. 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, supervised by 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 an RAEng Research Fellowship, 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 Robot Learning. In a part-time external role, I am Head of Robot Learning at Dyson.

I am a PhD student at Imperial College London, working in the Robot Learning Lab under the supervision of Dr. Edward Johns. In 2014, I received a Bachelors (BSc) with Honours in Physics  from the University of St. Andrews. After working as an Implementation Engineer at Apttus for a year, I completed a Masters (MSc) in Computer Science at Imperial College. More recently, I worked as a software developer for Prof. Kazunori Sakamoto at the National Institute of Informatics in Tokyo, and received a Masters (MSc) in Machine Learning from University College London. For my Masters dissertation, I worked under the supervision of Prof. Gabriel Brostow on predicting the future through semantic object segmentation. My current research interests lie at the crossroads of Computer Vision, Reinforcement Learning and Robotics, with particular emphasis on sim-to-real transfer for robot manipulation tasks.

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Publications

CoRL 2021 Learning Eye-in-Hand Calibration from a Single Image

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

IROS 2020 Crossing the Gap: A Deep Dive into Zero Shot Sim-to-Real Transfer for Dynamics

I am currently a PhD student at Imperial College London working in the Hamlyn Centre and the Robot Learning Lab. I received my Bachelor’s in Biomedical Engineering in 2013 at University of Melbourne. Later, I received two Master’s degrees, in Mechatronics Engineering at University of Melbourne and in Medical Robotics and Image Guided Intervention at Imperial College London, both completed with Distinction. My research interest is in robot learning and adaption for complex tasks in surgical applications. The focus of the research is to achieve surgical task automation through Computer Vision, Reinforcement Learning, and Learning from Demonstrations.

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Publications

ICRA 2021 DROID: Minimizing the Reality Gap Using Single-Shot Human Demonstration

ICRA 2020 Constrained Space Optimization and Reinforcement Learning for Complex Tasks

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.

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Publications

CoRL 2021 Learning Eye-in-Hand Calibration from a Single Image

IROS 2021 Hybrid ICP

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

CoRL 2021 Learning Multi-Stage Tasks with One Demonstration via Self-Replay

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.

I am a PhD student at Imperial College London jointly supervised by Dr. Edward Johns at the Robot Learning Lab and Prof. Andrew Davison at the Dyson Robotics Lab. Before joining the team, I graduated with First Class Honours from the four-year Computing MEng programme at Imperial College. My MEng thesis was supervised by Dr. Edward Johns. It focused on learning tidying preferences using techniques such as graph neural networks, variational autoencoders and word embeddings from natural language processing. My research is centred around aligning robot goals with human interests. This includes ideas from Inverse Reinforcement Learning and Computer Vision, as well as novel methods which learn with minimal human supervision.

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Publications

CoRL 2021 My House My Rules: Learning Tidying Preferences with Graph Neural Networks

Before joining the Robot Learning lab, I obtained a BSc. in Computer Science from the University of Surrey in 2020, where I worked on improving the performance of adversarial Imitation Learning (IL) methods by formulating IL as minimisation of the Sinkhorn distance between the demonstrator’s and learner’s trajectories. Following my BSc., I obtained an MPhil in Advanced Computer Science from the University of Cambridge. As an MPhil student I spent the majority of my time conducting research which focused on developing an automatic curriculum learning method for Reinforcement learning agents. Currently for my PhD, my research is focused on developing new methods to teach robots to perform contact-rich manipulation tasks in the real-world efficiently from human demonstrations.