probabilistic-modelling

Model-based reinforcement learning under uncertainty

In this talk I’ll present our recent paper that has just been accepted into AISTATS 2023, titled Mode-Constrained Model-Based …

Feb 13, 2023 11:30 AM — 12:30 PM University of Cambridge
Aidan Scannell
Project Slides
Model-based reinforcement learning under uncertainty
Bayesian Learning for Control in Multimodal Dynamical Systems
Mode remaining navigation (and exploration) in unknown multimodal dynamical systems via model-based reinforcement learning.
Aidan Scannell
PDF Cite Code Slides Source Document
Bayesian Learning for Control in Multimodal Dynamical Systems
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”.
Jun 3, 2021 11:00 AM — 11:30 AM Xi'an, China
Aidan Scannell
Code Project Video Follow
Identifiable Mixtures of Sparse Variational Gaussian Process Experts
Mixture models are inherently unidentifiable as different combinations of component distributions and mixture weights can generate the …
Aidan Scannell, Carl Henrik Ek, Arthur Richards
PDF Code Project
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, Carl Henrik Ek, Arthur Richards
PDF Cite Code Video
Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation
Synergising Bayesian Inference and Probabilistic Geometries for Robotic Control
This talk presented recent work synergising Bayesian inference and probabilistic Riemannian geometries to control multimodal dynamical …
Mar 18, 2021 9:00 AM — 10:00 AM Cognitive Systems - Technical University of Denmark (DTU)
Aidan Scannell
PDF Code Project Slides Follow
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 trajectories that remain in a preferred dynamics mode where possible and in regions of the transition dynamics model that have been observed and can be predicted confidently.
Aidan Scannell
Code Follow
Trajectory Optimisation in Learned Multimodal Dynamical Systems
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 implemented in GPflow/TensorFlow.
Aidan Scannell
Code Follow
Identifiable Mixtures of Sparse Variational Gaussian Process Experts
Gaussian Process Regression
This post introduces the theory underpinning Gaussian process regression and provides a basic walk-through in python.
Aidan Scannell
Oct 1, 2019 22 min read
Project
Gaussian Process Regression