A popular approach to high dimensional control problems in robotics uses a library of candidate \u201cmaneuvers\u201d or \u201ctrajectories\u201d. 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 the list is searched before a successful candidate is found. Our method can be used in both on-line and batch settings with provable performance guarantees, and can be run in an anytime manner to handle real-time constraints.<\/p>\n","protected":false},"excerpt":{"rendered":"
A popular approach to high dimensional control problems in robotics uses a library of candidate \u201cmaneuvers\u201d or \u201ctrajectories\u201d. 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). 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