The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

A popular approach to high dimensional control problems in robotics uses a library of candidate “maneuvers” or “trajectories”. The library is either evaluated on a ﬁxed number of candidate choices at runtime (e.g. path set selection for planning) or by iterating through a sequence of feasible choices until success is achieved (e.g. grasp selection). The performance of the library relies heavily on the content and order of the sequence of candidates. We propose a provably efﬁcient method to optimize such libraries, leveraging recent advances in optimizing sub-modular functions of sequences. This approach is demonstrated on two important problems: mobile robot navigation and manipulator grasp set selection. In the ﬁrst case,performance can be improved by choosing a subset of candidates which optimizes the metric under consideration (cost of traversal). In the second case, performance can be optimized by minimizing the depth in the list that is searched before a successful candidate is found. Our method can be used in both online and batch settings with provable performance guarantees, and can be run in an anytime manner to handle real-time constraints.

Keywords

submodularity, control libraries, path planning, manipulation, optimization

Notes

Sponsor: Army Research Laboratories through R-CTA and DARPA through ARM-S

Text Reference

Debadeepta Dey, Tommy Liu, Boris Sofman, and J. Andrew (Drew) Bagnell, "Efficient Optimization of Control Libraries," 26th Conference of Association for Advancement of Artificial Intelligence, July, 2012.

BibTeX Reference

@inproceedings{Dey_2012_7241,
   author = "Debadeepta Dey and Tian Yu Liu and Boris Sofman and J. Andrew (Drew) Bagnell",
   title = "Efficient Optimization of Control Libraries",
   booktitle = "26th Conference of Association for Advancement of Artificial Intelligence",
   month = "July",
   year = "2012",
}

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