Robust optimization with applications to design of context specific robot solutions

This paper presents an investigation of five optimization algorithms for simulation-based optimization for robotic tasks, where robust solutions are required. We evaluate the optimization methods on three use cases. The use cases involve using a robot for handling meat, optimizing gripper design for aligning objects and optimizing gripper design for table picking in cluttered scenes. We use dynamic simulations to model the use cases, where the most important physical aspects are captured. We have a focus on the robustness with respect to crucial system uncertainties, which is important in an industrial setting. The choice of parameterization and objective scores is also discussed since this choice has some impact on the performance of the optimization algorithms. For all problems, we find feasible solutions ready for real world testing, and overall the optimization method RBFopt has the best performance in terms of finding robust solutions within the fewest amount of simulations.