Reasoning for robot control can be learned by treating it as variational inference over a latent space, allowing policies to adaptively allocate computation at test time while maintaining spatial understanding needed for precise physical actions.
This paper introduces Latent Memory Palace (LMP), a method that enables control policies to reason adaptively by organizing information in a learned latent space. Rather than reasoning in language, the approach uses variational inference to create an interpretable, memory-palace-like structure where the policy retrieves information iteratively.