Efficient Optimization of Control Libraries
Abstract
A popular approach to high dimensional control problems in robotics uses a library of candidate maneuvers or trajectories [13, 28]. 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 [29]. 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 online and batch settings with provable performance guarantees, and can be run in an anytime manner to handle real-time constraints.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2011
- Accession Number
- ADA592141
Entities
People
- Boris Sofman
- Debadeepta Dey
- Drew Bagnell
- Tian Y. Liu
Organizations
- Carnegie Mellon University