Dr Edward Johns is the Director of the Robot Learning Lab at Imperial College London, and a Senior Lecturer (Associate Professor). His work lies at the intersection of robotics, computer vision, and machine learning, and he and his team are currently studying visually-guided robot manipulation.
He received a BA and MEng in Electrical and Information Engineering from Cambridge University, and a PhD in visual place recognition from Imperial College. Following his PhD, he was a post-doc at UCL, before returning to Imperial College as a founding member of the Dyson Robotics Lab with Prof Andrew Davison, where he led the robot manipulation research.
In 2017, he was awarded a prestigious Royal Academy of Engineering Research Fellowship for his project "Empowering Next-Generation Robots with Dexterous Manipulation: Deep Learning via Simulation", and then in 2018 he was appointed as a Lecturer and founded the Robot Learning Lab. He established Imperial College's first course on Robot Learning, which he currently teaches at graduate level.
He has published over 50 peer-reviewed papers, which have over 2500 citations. In 2022 he received Imperial College's President's Award for Outstanding Early Career Researcher. Externally, he is on the advisory board for a number of robotics startups, including Karakuri and Muddy Machines, and from 2021 to 2022 he spent a year as Head of Robot Learning at Dyson in a part-time role.
Johns E, Di Palo N, 2021, Learning multi-stage tasks with one demonstration via self-replay, Conference on Robot Learning (CoRL) 2021, OpenReview, Pages:1-10
Johns E, Coarse-to-fine imitation learning: robot manipulation from a single demonstration, 2021 International Conference on Robotics and Automation (ICRA), Institute of Electrical and Electronics Engineers, ISSN:1050-4729
James S, Davison A, Johns E, 2017, Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task, Conference on Robot Learning, PMLR, Pages:334-343
Johns E, Leutenegger S, Davison AJ, 2016, Pairwise Decomposition of Image Sequences for Active Multi-View Recognition, Computer Vision and Pattern Recognition, Computer Vision Foundation (CVF), ISSN:1063-6919
Johns E, Leutenegger S, Davison AJ, 2016, Deep learning a grasp function for grasping under gripper pose uncertainty, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Pages:4461-4468, ISSN:2153-0866