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Sequence optimization, where the items in a list are ordered to maximize somereward has many applications such as web advertisement placement, search, andcontrol libraries in robotics. Previous work in sequence optimization producesa static ordering that does not take any features of the item or context of theproblem into account. In this work, we propose a general approach to order theitems within the sequence based on the context (e.g., perceptual information,environment description, and goals). We take a simple, efficient,reduction-based approach where the choice and order of the items is establishedby repeatedly learning simple classifiers or regressors for each "slot" in thesequence. Our approach leverages recent work on submodular functionmaximization to provide a formal regret reduction from submodular sequenceoptimization to simple cost-sensitive prediction. We apply our contextualsequence prediction algorithm to optimize control libraries and demonstrateresults on two robotics problems: manipulator trajectory prediction and mobilerobot path planning.

Predicting Contextual Sequences via Submodular Function Maximization