/Predicting Contextual Sequences via Submodular Function Maximization

Predicting Contextual Sequences via Submodular Function Maximization

Debadeepta Dey, Tommy Liu, Martial Hebert and J. Andrew (Drew) Bagnell
Tech. Report, CMU-RI-TR-12-05, Robotics Institute, Carnegie Mellon University, February, 2012

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.


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.

BibTeX Reference
author = {Debadeepta Dey and Tommy Liu and Martial Hebert and J. Andrew (Drew) Bagnell},
title = {Predicting Contextual Sequences via Submodular Function Maximization},
year = {2012},
month = {February},
institution = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-12-05},
keywords = {submodular, reduction, optimization, robotics, control, manipulation, path planning, navigation, perception},