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Team Members

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Ed Murat in lab
Eugene in lab

Edward

Johns

Kamil

Dreczkowski

Shikun

Liu

Norman

Di Palo

Vitalis

Vosylius

Ivan

Kapelyukh

Georgios

Papagiannis

Pietro

Vitiello

Yifei

Ren

Naoki

Kiyohara

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Edward Johns

Lab Director

e.johns@imperial.ac.uk

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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.

Kamil Dreczkowski

PhD student (started 2019)

 

kamil.dreczkowski15@imperial.ac.uk

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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.

Publications

CoRL 2023 One-Shot Imitation Learning: A Pose Estimation Perspective

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

IROS 2021 Hybrid ICP

Shikun Liu

PhD student (started 2019)

shikun.liu17@imperial.ac.uk

Personal Website

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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.

Publications

TMLR 2024 Prismer: A Vision-Language Model with Multi-Task Experts

TMLR 2022 Auto-λ: Disentangling Dynamic Task Relationships

ICLR 2022 Bootstrapping Semantic Segmentation with Regional Contrast

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

Norman Di Palo

PhD student (started 2020)

n.di-palo20@imperial.ac.uk

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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.

Publications

RA-L 2024 On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation

IROS 2022 Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning

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

Vitalis Vosylius

PhD student (started 2020)

 

vitalis.vosylius19@imperial.ac.uk
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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.

Publications

CoRL 2023 Few-Shot In-Context Imitation Learning via Implicit Graph Alignment

RA-L 2023 DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics
CoRL 2022 Where to Start? Transferring Simple Skills to Complex Environments

Ivan Kapelyukh

PhD student (started 2021)

ivan.kapelyukh17@imperial.ac.uk

Personal Website

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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.

Publications

ICRA 2024 Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Models

RA-L 2023 DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics

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

Georgios Papagiannis

PhD student (started 2021)

g.papagiannis21@imperial.ac.uk

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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.

Publications

IROS 2022 Demonstrate Once, Imitate Immediately: Learning Visual Servoing for One-Shot Imitation Learning

Pietro Vitiello

PhD student (started 2022)

pietro.vitiello17@imperial.ac.uk

Personal Website

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I am an Italian currently pursuing a PhD in the Robot Learning Lab at Imperial College London. In 2021 I graduated from Imperial College with an MEng in Biomedical Engineering, during which I worked on several projects which opened my eyes to the fascinating topic of machine learning and computer vision. To strengthen my foundations I subsequently decided to take an MSc in Artificial Intelligence and Machine Learning at Imperial College, from which I graduated in 2022. In the last months of this Masters degree I had the pleasure to research towards finding an alternative visual representation for robot actions in visual robot learning. Currently, my interests lie at the intersection of Robotics and Computer Vision and are focused on few-shot Imitation Learning for robot manipulation. Ultimately, I aim at developing methods through which a robot can learn everyday object interaction skills, while minimising the required amount of human supervision.

Publications

CoRL 2023 One-Shot Imitation Learning: A Pose Estimation Perspective

Yifei Ren

PhD student (started 2022)

yifei.ren20@imperial.ac.uk

Twitter

Google Scholar

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I'm a PhD student at Imperial College London, working in the Robot Learning Lab supervised by Dr. Edward Johns. I received my BEng degree in Intelligent Science and Technology from Nankai University in China, where I worked on Robotics and Computer Vision. After that, I completed my MSc with Distinction at Imperial College London, during which my main focus was on object-level SLAM system. Currently, my interests lie in Robotics, Computer Vision and SLAM, and my current research is focussed on the intersection of 3D Computer Vision and Robot Manipulation, such as bringing 3D information to Imitation Learning frameworks to enable high accuracy

Publications

ICRA 2024 Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Models

Naoki Kiyohara

PhD student (started 2023)

n.kiyohara23@imperial.ac.uk

Google Scholar

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I am from Tokyo, Japan. I graduated with a bachelor's degree in Physics at Tokyo University of Science and a master’s degree in Advanced Materials Science at the University of Tokyo. Subsequently, I joined Canon Inc. (Tokyo) and am now at Imperial College London as a PhD student, sponsored through the company's corporate study abroad program. My research is centered around model-based reinforcement learning, with a particular focus on sequential generative models. Moving forward, I aim to significantly enhance exploration efficiency by utilizing models that can capture uncertainties, such as Bayesian models. In my free time, I enjoy playing and listening to classical piano music. I am co-supervised by Edward Johns and Yingzhen Li.

Edward Johns
Kamil Dreczkowski
Shikun Liu
Norman Di Palo
Vitalis Vosylius
Ivan Kapelyukh
Georgios Papagiannis
Pietro Vitiello
Yifei Ren
Naoki Kayohara

Undergraduate and Master's Students (2023-2024)

MRes: Yilong Wang
MEng: Alexandru Birtoiu, Alfie Chenery, Edward Hartley, Yutong Jiang, Bowen Li, Andrei Tudor
MSc: Matis Bodaghi, Sarthak Das, Mikolaj Deja, Wentao Ma, Jiayi Nie

Alumni

PhD Students

Eugene Valassakis, PhD student from 2018 - 2022, now at Niantic. Read about Eugene's PhD research by clicking here.
 

Undergraduate and Master's Individual Project Students

MSc 2022-2023: Lucas Doust Alba, Vivek Jagannath, Ugo Okoroafor
MEng 2022-2023: Haopeng Luo, Nasir Majid, Jia Qi Poon, Apoorva Verma
MSc 2021-2022: John Carter, Hubert Dellon, Emir Sahin, Kejian Shi, Pietro Vitiello, Xintong Yu
MEng 2021-2022: Ella Bettison, Marco de Freitas, Aditya Goel, Himanshu Nagar, Ruari Phipps, Edward Williams
MPhil 2019-2021: Murat Uzun
MEng 2020-2021: Ivan Kapelyukh, Arjun Narula
MSc 2020-2021: Vadim Becquet, Jing Hu, Raul Steleac, Altan Tutar, Qianzeng Yang

MEng 2019-2020: Pablo Gorostiaga Belio
MSc 2019-2020: Raghad Alghonaim, Omar Elkhatib, Zhen Lee, Alvaro Prat Balasch, Chris Smith, Vitalis Vosylius
MEng 2018-2019: Steven Battilana, Fraser Price, Silvia Sapora, Harry Uglow
MSc 2018-2019: Sirapoab Chaikunsaeng, Zihan Ding, Kamil Dreczkowski, Mark Hartenstein, Georgios Theodorou, Maco Wong
ISO 2018-2019: Meiru Zhang

MRes 2017-2018: Shikun Liu
MEng 2017-2018: SiCong Li
MSc 2017-2018: Linkun Geng, Ludovico Lazzaretti
MEng 2016-2017: Miklos Kepes, Rad Ploshtakov
MSc 2016-2017: Jonathan Heng, Jiaxi Liu, Diego Mendoza Barrenechea
MEng 2015-2016: Stephen James, Christophe Steininger

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