Model-based Reinforcement Learning, Robotics, Optimal Control, Information Theory, Generative Models, Dynamical Systems, Embedded Robotics
TU Darmstadt, FG IAS,
Hochschulstr. 10, 64289 Darmstadt
Office. Room E327, Building S2|02
Joe joined the Intelligent Autonomous Systems Group at TU Darmstadt as a Ph.D. researcher in December 2018. He studied Information and Computer Engineering at the University of Cambridge, where he received his BA and MEng. His Master’s thesis “Vision-Based Learning for Robotic Grasping”, which investigated the use of Convolutional Neural Networks for real-world grasp prediction, was undertaken at the Bio-Inspired Robotics Lab (BIRL) under the supervision of Fumiya Iida. For two years, Joe worked at CMR Surgical (previously Cambridge Medical Robotics), a medical device startup, as an Embedded Software Engineer. He worked extensively on the Control and Signal Processing stack of the manipulators of Versius, a bespoke robotic platform for laparoscopic surgery.
Working on the SKILLS4ROBOTS project, Joe is researching the development of principled algorithms that facilitate robot learning of complex tasks in unstructured settings. He is currently looking into model-based reinforcement learning, inductive biases for robot learning and control-as-inference.
Robotics, Model-based Reinforcement Learning, Optimal Control, Information Theory, Generative Models, Dynamical Systems, Embedded Robotics
!!Key References # Swiezinski, L. (2013). Lifecycle of a Jeopardy Question Answered by Watson DeepQA, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar. See Details [Details] Download Article [PDF] BibTeX Reference [BibTex]