Aidan Scannell
Aidan Scannell
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Mode-Constrained Exploration for Model-Based Reinforcement Learning
This work presents a learning-based control method for navigating to a target state in unknown, or partially unknown, multimodal dynamical systems. In particular, it develops a model-based reinforcement learning algorithm that can remain in a desired dynamics mode with high probability. For example, if some of the dynamics modes are believed to be inoperable.
Aidan Scannell
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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
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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
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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
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Carl Henrik Ek
,
Arthur Richards
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GPJax - Gaussian Processes in Jax
Minimal Gaussian process library in JAX with a simple (custom) approach to state management.
Aidan Scannell
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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
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Carl Henrik Ek
,
Arthur Richards
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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
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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
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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 implemented in GPflow/TensorFlow.
Aidan Scannell
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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
Oct 1, 2019
22 min read
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