Contextual Sequence Optimization with Application to Control Library Optimization

Debadeepta Dey, Tommy Liu, Martial Hebert, and J. Andrew (Drew) Bagnell
Robotics Science and Systems, August, 2012.


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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
Manipulation, Path Planning, Grasping, Sequence Prediction, Submodularity, Machine Learning

Notes
Sponsor: DARPA thro9ugh ARM-S and ONR through BIRD MURI

Text Reference
Debadeepta Dey, Tommy Liu, Martial Hebert, and J. Andrew (Drew) Bagnell, "Contextual Sequence Optimization with Application to Control Library Optimization," Robotics Science and Systems, August, 2012.

BibTeX Reference
@inproceedings{Dey_2012_7251,
   author = "Debadeepta Dey and Tian Yu Liu and Martial Hebert and J. Andrew (Drew) Bagnell",
   editor = "Siddhartha Srinivasa",
   title = "Contextual Sequence Optimization with Application to Control Library Optimization",
   booktitle = "Robotics Science and Systems",
   month = "August",
   year = "2012",
}