The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Sequence optimization, where the items in a list are ordered to maximize some reward has many applications such as web advertisement placement, search, and control libraries in robotics. Previous work in sequence optimization produces a static ordering that does not take any features of the item or context of the problem into account. In this work, we propose a general approach to order the items 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 established by repeatedly learning simple classifiers or regressors for each ``slot'' in the sequence. Our approach leverages recent work on submodular function maximization to provide a formal regret reduction from submodular sequence optimization to simple cost-sensitive prediction. We apply our contextual sequence prediction algorithm to optimize control libraries and demonstrate results on two robotics problems: manipulator trajectory prediction and mobile robot path planning.

Keywords

submodular, reduction, optimization, robotics, control, manipulation, path planning, navigation, perception

Notes

Text Reference

Debadeepta Dey, Tommy Liu, Martial Hebert, and J. Andrew (Drew) Bagnell, "Predicting Contextual Sequences via Submodular Function Maximization," tech. report CMU-RI-TR-12-05, Robotics Institute, Carnegie Mellon University, February, 2012

BibTeX Reference

@techreport{Dey_2012_6975,
   author = "Debadeepta Dey and Tian Yu Liu and Martial Hebert and J. Andrew (Drew) Bagnell",
   title = "Predicting Contextual Sequences via Submodular Function Maximization",
   booktitle = "",
   institution = "Robotics Institute",
   month = "February",
   year = "2012",
   number= "CMU-RI-TR-12-05",
   address= "Pittsburgh, PA",
}

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