Event Location: Newell Simon Hall 1507
Bio: Ed Johns is a Dyson Fellow at Imperial College London, working on computer vision, robotics and machine learning. He received a BA and MEng from Cambridge University, followed by a PhD in visual recognition and localisation from Imperial College London. After post-doctoral work at University College London, he then took up a research fellowship and returned to Imperial to help set up the Dyson Robotics Lab with Professor Andrew Davison. He now works on visually-guided robot manipulation for domestic robotics.

Abstract: In recent years, deep learning has begun to dominate computer vision research, with convolutional neural networks becoming the standard machine learning tool for a wide range of tasks. However, one of the requirements for these methods to work effectively is a rich source of training data. Therefore, parallel applications in “real-world” robotics such as manipulation, are often still limited by the capacity to generate large-scale, high-quality data. In this talk, I will introduce some techniques I have developed to train robots using simulation, without the need to conduct costly real-world experiments. Specifically, I will talk about multi-view active object recognition, robotic grasping using physics simulation, and deep reinforcement learning for robotic arm control.