Name
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 lirst 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICRA48506.2021.9561362 | 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) |
DocType | Volume | Issue |
Conference | 2021 | 1 |
ISSN | Citations | PageRank |
1050-4729 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aidan Scannell | 1 | 0 | 0.34 |
| carl henrik ek | 2 | 327 | 30.76 |
| Arthur Richards | 3 | 1 | 2.39 |