aidan.bib

@inproceedings{scannellTrajectory2021,
  title           = {Trajectory {Optimisation} in {Learned} {Multimodal}
                  {Dynamical} {Systems} {Via} {Latent}-{ODE} {Collocation}},
  abstract        = {This paper 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 to learn a predictive dynamics model from historical
                  data. Importantly, this model learns a gating function that
                  indicates the probability of being in a particular dynamics
                  mode at a given state location. This gating function acts as a
                  coordinate map for a latent Riemannian manifold on which
                  shortest trajectories are solutions to our trajectory
                  optimisation problem. Based on this intuition, the second
                  stage formulates a geometric cost function, which it then
                  implicitly minimises by projecting the trajectory optimisation
                  onto the second-order geodesic ODE; a classic result of
                  Riemannian geometry. A set of collocation constraints are
                  derived that ensure trajectories are solutions to this ODE,
                  implicitly solving the trajectory optimisation problem.},
  booktitle       = {Proceedings of the {IEEE} {International} {Conference} on
                  {Robotics} and {Automation}},
  publisher       = {IEEE},
  author          = {Aidan Scannell and Carl Henrik Ek and Arthur Richards},
  year            = {June 2021},
}