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Model-Free Design Optimization of a Hopping Robot and Its Comparison With a Human Designer

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Saar, Kaur 
Giardina, Fabio 

Abstract

When developing a robot, design iterations in the physical world are necessary, even though they are often costly and not systematic. Here, we present an automated iterative design process without using modeling or simulation, which we refer to as “model-free design optimization” based on Bayesian optimization. This letter particularly focuses on the cooptimization of morphology and controller, by using a mechanism to balance parameter specific costs (i.e., morphology samplings are more expensive than control ones) for effective and efficient design optimization processes. A hopping robot was employed for a feasibility analysis of the proposed optimization method, in which minimalistic two-dimensional and four-dimensional design optimization experiments were performed in real life. The results show that the proposed approach is capable of improving both of the robot design problems within a defined time limit. The method is also compared to optimization performances of a human designer under the same conditions. The automated method has advantage in finding the best solution more quickly in the four-dimensional search space, while the human optimization performs better in the two-dimensional case.

Description

Keywords

legged robots, optimization and optimal control, probability and statistical methods, product design, development and prototyping

Journal Title

IEEE Robotics and Automation Letters

Conference Name

Journal ISSN

2377-3766
2377-3766

Volume Title

3

Publisher

IEEE
Sponsorship
Engineering and Physical Sciences Research Council (EP/N029003/1)
This work was supported by the U.K. Engineering and Physical Sciences Research Council under Grant EP/N03211X/1.