- Statistical methods for learning control
- Bayesian methods and approximate inference
- Differentiable physics and robotic priors
Joe joined the Intelligent Autonomous Systems Group at TU Darmstadt as a Ph.D. researcher in December 2018. He studied Information & 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 Dr Fumiya Iida. For two years, Joe worked at CMR Surgical (previously Cambridge Medical Robotics), a medical device startup. He worked extensively on the control and signal processing stack of the manipulators of Versius, a bespoke robotic platform for laparoscopic surgery, which to date has performed over 10,000 surgical procedures globally.
- Msc, Fabio D’Aquino Hilt (with Joao Carvalho)
- Msc, Jihao Andreas Lin (with Pascal Klink)
- Msc, Thomas Gossard (with Michael Lutter)
- Msc, Yannick Eich (with Hany Abdulsamad)
- Msc, Johannes Silberbauer (with Michael Lutter)
- Msc, Len Williamson
- Bsc, Darya Nikitina
- Bsc, Amin Ali
- Bsc, Fabian Damken
Robot Learning teaching assistant (2020 - 2022)
CoRL, NeurIPS, ICML, ICLR, AISTATS, IROS, IEEE RAL, Neurocomputing
Joe is interested in the duality between entropy-regularized optimization and (psuedo-) Bayesian inference for robot learning, as a means of designing principled methods for complex learning tasks. The application of these techniques range from trajectory optimization, imitation learning and model-free reinforcement learning. The Bayesian methodology also motivate the careful design of models and priors, which has inspired research involving Gaussian processes, differentiable physics simulators and function-space inference techniques.
Control as Inference
- , Conference on Robot Learning (CoRL).
- , Conference on Robot Learning (CoRL 2019).
- , American Control Conference (ACC).
- , The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).
Bayesian Machine Learning
- , 3rd Symposium on Advances in Approximate Bayesian Inference (AABI).
- , Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).
Inductive Biases and Differentiable Physics
- , Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
- , R:SS Workshop: Differentiable Simulation for Robotics.
- , IEEE Robotics and Automation Letters (R-AL), 7, 1, pp.478-485.