Learning the Problem-Optimum Map: Analysis and Application to Global Optimization in Robotics

Kris Hauser



TRO Session (Oral)


09:55 - 11:10 | Tue 30 May | Room 4811/4812 | TUA9

Perception and Planning

Full Text


This paper describes a data-driven framework for approximate global optimization in which precomputed solutions to a sample of problems are retrieved and adapted during online use to solve novel problems. This approach has promise for realtime applications in robotics, since it can produce near-globally optimal solutions orders of magnitude faster than standard methods. This paper establishes theoretical conditions on how many and where samples are needed over the space of problems to achieve a given approximation quality. The framework is applied to solve globally optimal collision-free inverse kinematics (IK) problems, wherein large solution databases are used to produce near-optimal solutions in sub-millisecond time on a standard PC.

Additional Information

No manuscript is included in ICRA proceedings for TRO presentations.


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