PhD Thesis: Bayesian Learning for Control in Multimodal Dynamical Systems

This repository contains the org and LaTeX source for my PhD thesis.

Publications & Code

Some of the work in my thesis is published in:

Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation Aidan Scannell, Carl Henrik Ek, Arthur Richards Published in 2021 IEEE International Conference on Robotics and Automation (ICRA)

Mode-constrained Model-based Reinforcement Learning via Gaussian Processes Aidan Scannell, Carl Henrik Ek, Arthur Richards Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS)

The work in each of the content chapters is roughly split into the following code bases:

Identifiable Mixtures of Sparse Variational Gaussian Process Experts Aidan Scannell, Carl Henrik Ek, Arthur Richards

Mode Remaining Trajectory Optimisation Aidan Scannell, Carl Henrik Ek, Arthur Richards

Mode-constrained Model-based Reinforcement Learning via Gaussian Processes Aidan Scannell, Carl Henrik Ek, Arthur Richards

Instructions to Build PDF

Generate phd-thesis.pdf from phd-thesis.org using

emacs --batch -l init.el phd-thesis.org -f org-latex-export-to-pdf --kill

This uses the Emacs LaTeX exporter so I provide a minimal Emacs configuration in init.el and export to pdf in batch mode.

The Dockerfile creates a working environment which can be built with

docker build -t emacs-image .

Cite

@phdthesis{scannell22,
    title = {Bayesian Learning for Control in Multimodal Dynamical Systems},
    author = {Aidan Scannell},
    school = {University of Bristol},
    year = {2022}}