Model-based reinforcement learning under uncertainty
Aidan Scannell
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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".
Aidan Scannell
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1 min readIdentifiable Mixtures of Sparse Variational Gaussian Process Experts
Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation
Synergising Bayesian inference and Riemannian geometry for control in multimodal dynamical systems.
Synergising Bayesian Inference and Probabilistic Geometries for Robotic Control
Aidan Scannell
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1 min read
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 …
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 …
Gaussian Process Regression
This post introduces the theory underpinning Gaussian process regression and provides a basic walk-through in python.