Expensive multiobjective optimization for robotics

Many practical optimization problems in robotics involve multiple competing objectives - from design trade-offs to performance metrics of the physical system such as speed and energy efficiency. Proper treatment of these objective functions, while commonplace in fields such as economics, is often overlooked in robotics. Additionally, optimization of the performance of robotic systems can be restricted due to the expensive nature of testing control parameters on a physical system. This paper presents a multi-objective optimization (MOO) algorithm for expensive-to-evaluate functions that generates a Pareto set of solutions. This algorithm is compared against another leading MOO algorithm, and then used to optimize the speed and head stability of the sidewinding gait for a snake robot.

Authors

• Matthew Tesch
• Jeff G. Schneider
• Howie Choset

Keywords

• Estimation
• Multi-objective Optimization
• Expensive Optimization
• Objective Function
• Optimization Algorithm
• Physical System
• Robotic System
• Multi-objective Optimization Algorithm
• Problem In Robotics
• Pareto Set
• Head Stabilization
• Function Tests
• Optimization Method
• Parameter Space
• Global Optimization
• Multiple Objects
• Gaussian Process
• Single Object
• Pareto Optimal
• Objective Space
• Pareto Front
• Pareto Optimal Set
• Single-objective Optimization
• Hypervolume
• Constrained Method
• Expensive Function
• Gradient Information
• Surrogate Function
• Single Optimization
• Single Objective Function
• High-dimensional Parameter Space