{"id":247715,"date":"2012-07-01T20:09:40","date_gmt":"2012-07-02T03:09:40","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=247715"},"modified":"2018-10-16T20:15:20","modified_gmt":"2018-10-17T03:15:20","slug":"efficient-optimization-control-libraries-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/efficient-optimization-control-libraries-2\/","title":{"rendered":"Efficient Optimization of Control Libraries"},"content":{"rendered":"
\n
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\u00a0on a fixed number of candidate choices at runtime (e.g.\u00a0path set selection for planning) or by iterating through\u00a0a sequence of feasible choices until success is achieved\u00a0(e.g.\u00a0grasp selection). The performance of the library\u00a0relies heavily on the content and order of the sequence\u00a0of candidates. We propose a provably efficient method\u00a0to optimize such libraries, leveraging recent advances\u00a0in optimizing sub-modular functions of sequences. This\u00a0approach is demonstrated on two important problems:\u00a0mobile robot navigation and manipulator grasp set selection. In the first case, performance can be improved\u00a0by choosing a subset of candidates which optimizes the\u00a0metric under consideration (cost of traversal). In the\u00a0second case, performance can be optimized by minimizing the depth in the list that is searched before a\u00a0successful candidate is found. Our method can be used\u00a0in both on-line and batch settings with provable performance guarantees, and can be run in an anytime manner\u00a0to handle real-time constraints.<\/div>\n<\/div>\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\u00a0on a fixed number of candidate choices at runtime (e.g.\u00a0path set selection for planning) or by iterating through\u00a0a sequence of feasible choices until success is achieved\u00a0(e.g.\u00a0grasp selection). The performance of the library\u00a0relies heavily […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Association for the Advancement of Artificial 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