
A "generate-and-test" approach to risk-bounded planning for autonomous mobile agents with learned, non-linear, stochastic dynamics. The method uses a variational autoencoder to learn an approximate linear latent dynamics model and performs trajectory optimization in the latent space, while a validator assesses the risk of the candidate trajectory and computes additional safety constraints.


coremsg

latent-space-planning-for-stochastic-systems-an-efficient-approach-to-risk-bounded-trajectory-optimization-with-learned-dynamics


Latent Space Planning for Stochastic Systems: An Efficient Approach to Risk-Bounded Trajectory Optimization with Learned Dynamics
