Joe Watson

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.

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 interested in inference methods for control, model-based reinforcement learning and inductive biases for robot learning.

Research Interests

robotics, optimal control, model-based reinforcement learning, Bayesian machine learning, approximate inference, inductive biases

Key References

Control as Inference

1. Watson, J.; Peters, J. (2022). Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes, Conference on Robot Learning.   Download Article [PDF]   BibTeX Reference [BibTex]
2. Watson, J.; Abdulsamad, H.; Peters, J. (2019). Stochastic Optimal Control as Approximate Input Inference, Conference on Robot Learning (CoRL 2019).   Download Article [PDF]   BibTeX Reference [BibTex]
3. Watson, J.; Peters, J. (2021). Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk, American Control Conference (ACC).   Download Article [PDF]   BibTeX Reference [BibTex]

Bayesian Machine Learning

1. Watson, J.; Lin, J. A.; Klink, P.; Peters, J. (2021). Neural Linear Models with Functional Gaussian Process Priors, 3rd Symposium on Advances in Approximate Bayesian Inference (AABI).   Download Article [PDF]   BibTeX Reference [BibTex]
2. Watson, J.; Lin J. A.; Klink, P.; Pajarinen, J.; Peters, J. (2021). Latent Derivative Bayesian Last Layer Networks, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).   Download Article [PDF]   BibTeX Reference [BibTex]

Inductive Biases

1. Lutter, M.; Silberbauer, J.; Watson, J.; Peters, J. (2021). Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).   Download Article [PDF]   BibTeX Reference [BibTex]

Talks

Stochastic Optimal Control as Approximate Input Inference
(CoRL 2019 Spotlight, Preferred Networks, RIKEN, ATR Institute) pdf

Supervision

• Msc, Len Williamson
• Msc, Johannes Silberbauer (with [[Team/Michael Lutter])
• Msc, Thomas Gossard (with [[Team/Michael Lutter])
• Msc, Jihao Andreas Lin (with Pascal Klink)
• Bsc, Fabian Damken
• Bsc, Darya Nikitina