Probabilistic-Modelling

Model-based reinforcement learning under uncertainty

PhD Thesis: Bayesian Learning for Control in Multimodal Dynamical Systems

Mode remaining navigation (and exploration) in unknown multimodal dynamical systems via model-based reinforcement learning.

Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation

In this talk I will present our ICRA 2021 paper "Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation".

Identifiable Mixtures of Sparse Variational Gaussian Process Experts

Aidan Scannell

• 26 May, 2021 • 1 min read

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Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation

Synergising Bayesian inference and Riemannian geometry for control in multimodal dynamical systems.

Aidan Scannell

• 22 Mar, 2021 • 1 min read

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Synergising Bayesian Inference and Probabilistic Geometries for Robotic Control

Trajectory Optimisation in Learned Multimodal Dynamical Systems

This work presents a two-stage method to perform trajectory optimisation in multimodal dynamical systems with unknown nonlinear stochastic transition dynamics. The method finds …

Aidan Scannell

• 16 Nov, 2020 • 1 min read

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Identifiable Mixtures of Sparse Variational Gaussian Process Experts

This work introduces a variational lower bound for the Mixture of Gaussian Process Experts model with a GP-based gating network based on sparse GPs. The model (and inference) are …

Aidan Scannell

• 16 Nov, 2020 • 1 min read

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Gaussian Process Regression

This post introduces the theory underpinning Gaussian process regression and provides a basic walk-through in python.

Aidan Scannell

• 1 Oct, 2019 • 22 min read

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