JAX

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".

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

This talk presented recent work synergising Bayesian inference and probabilistic Riemannian geometries to control multimodal dynamical systems (quadcopters). The work combines theory from probabilistic Riemannian geometry, that addressed issues of …

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. The first stage leverages a mixture of Gaussian process experts method (mogpe) written in GPflow/TensorFlow to learn a predictive dynamics model from historical data.