Abstract-Several categories of optimization problems suffer from expensive objective function evaluation, driving the need for smart selection of subsequent experiments. One such category of problems involves physical robotic systems, which often require significant time, effort, and monetary expenditure in order to run tests. To assist in the selection of the next experiment, there has been a focus on the idea of response surfaces in recent years. These surfaces interpolate the existing data and provide a measure of confidence in their error, serving as a low-fidelity surrogate function that can be used to more intelligently choose the next experiment. In this paper, we robustly implement a previous algorithm based on the response surface methodology with an expected improvement criteria. We apply this technique to optimize open-loop gait parameters for snake robots, and demonstrate improved locomotive capabilities.
In REF , Bayesian optimization is used to optimize the gait of a snake robot.
Using response surfaces and expected improvement to optimize snake robot gait parameters
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science