THIS EVENT HAS BEEN POSTPONED
Balancing Learning and Modelling in Robot Manipulation
Robot manipulation is the study of how robots can physically interact with objects in their environment, using their arms and hands. In recent years, a wide range of machine learning approaches have been developed for robot manipulation, including applications of deep learning and reinforcement learning. However, classical robot control, using engineered, model-based approaches, still has its place in robotics, yet it is often overlooked due to the recent popularity of machine learning. With a specific focus on vision-based robot manipulation, I will argue that finding the right balance of these two ideologies presents our best chance of making the first real breakthroughs for robotics in unstructured environments
Dr Edward Johns
Dr Edward Johns is the Director of the Robot Learning Lab at Imperial College London, where he is also a Lecturer and Royal Academy of Engineering Research Fellow. 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 postdoc at UCL, before returning to Imperial College as a founding member of the Dyson Robotics Lab along with Andrew Davison, where he led the robot manipulation team. In 2017, he was awarded a 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.
Seminar Series
The Robotics Forum is creating a series of seminar events to hear from roboticists from within Imperial College, and from guest lecturers. This event is brought to you in combination with the Imperial College Robotics Society – ICRS