Dr Edward Johns is the Director of the Robot Learning Lab at Imperial College London, where he and his team are developing the next generation of robots empowered with artificial intelligence. His research lies at the intersection of computer vision, machine learning, and robotics, with a particular focus on deep learning and reinforcement learning for robot manipulation. Applications include domestic robots (e.g. tidying the home), manufacturing robots (e.g. assembling products in a factory), and warehouse robots (e.g. picking and placing from/into storage). At Imperial College, he is also a Lecturer (Assistant Professor) and a Royal Academy of Engineering Research Fellow.
Edward Johns received a BA (2006) and MEng (2007) in Electrical and Information Engineering from Cambridge University, and a PhD (2014) in vision-based robot localisation from the Hamlyn Centre at Imperial College, working with Guang-Zhong Yang. Following his PhD, he spent a year as a postdoc at UCL working with Gabriel Brostow. In 2014, he then returned to Imperial College as a founding member of the Dyson Robotics Lab with Andrew Davison, where he held a Dyson Fellowship and led the lab's robot manipulation team. In 2017, he was awarded a Royal Academy of Engineering Research Fellowship for his work on deep learning for robot manipulation. He was then appointed as a Lecturer at Imperial College in 2018, and founded the Robot Learning Lab.
Johns E, Yang G-Z, 2014, Generative Methods for Long-Term Place Recognition in Dynamic Scenes, International Journal of Computer Vision, Vol:106, ISSN:0920-5691, Pages:297-314
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, ISSN:2153-0866