  Planning in learned latent spaces helps to decrease the dimensionality of raw
observations. In this work, we propose to leverage the ensemble paradigm to
enhance the robustness of latent planning systems. We rely on our Latent Space
Roadmap (LSR) framework, which builds a graph in a learned structured latent
space to perform planning. Given multiple LSR framework instances, that differ
either on their latent spaces or on the parameters for constructing the graph,
we use the action information as well as the embedded nodes of the produced
plans to define similarity measures. These are then utilized to select the most
promising plans. We validate the performance of our Ensemble LSR (ENS-LSR) on
simulated box stacking and grape harvesting tasks as well as on a real-world
robotic T-shirt folding experiment.
