Evolutionary Robotics and Open-Ended Design Automation

Topics: Evolution, Evolutionary algorithm, Mutation Pages: 38 (12119 words) Published: April 14, 2013
Evolutionary Robotics and Open-Ended Design Automation
Hod Lipson, Cornell University Can a computer ultimately augment or replace human invention? IMAGINE A LEGO SET AT YOUR DISPOSAL: Bricks, rods, wheels, motors, sensors and logic are your “atomic” building blocks, and you must find a way to put them together to achieve a given high-level functionality: A machine that can move itself, say. You know the physics of the individual components' behaviors; you know the repertoire of pieces available, and you know how they are allowed to connect. But how do you determine the combination that gives you the desired functionality? This is the problem of Synthesis. Although engineers practice it and teach it all the time, we do not have a formal model of how open-ended synthesis can be done automatically. Applications are numerous. This is the meta-problem of engineering: Design a machine that can design other machines. The example above is confined to electromechanics, but similar synthesis challenges occur in almost all engineering disciplines: Circuits, software, structures, robotics, control, and MEMS, to name a few. Are there fundamental properties of design synthesis that cut across engineering fields? Can a computer ultimately augment or replace human invention? While we may not know how to synthesize thing automatically, nature may give us some clues: After all, the fascinating products of nature were designed and fabricated autonomously.

In the last two centuries, engineering sciences have made remarkable progress in the ability to analyze and predict physical phenomena. We understand the governing equations of thermodynamics, elastics, fluid flow, and electromagnetics, to name but a few domains. Numerical methods such as finite elements allow us to solve these differential equations, with good approximation, for many practical situations. We can use these methods to investigate and explain observations, as well as to predict the behavior of products and systems long before they are ever physically realized. But progress in systematic synthesis has been much slower. For example, the systematic synthesis of a kinematic machine for a given purpose is a long-standing problem, and perhaps one of the earliest general synthesis problems to be posed. Robert Willis, a professor of natural and experimental philosophy at Cambridge, wrote in 1841 [32]: [A rational approach to synthesis is needed] to obtain, by direct and certain methods, all the forms and arrangements that are applicable to the desired purpose. At present, questions of this kind can only be solved by that species of intuition that which long familiarity with the subject usually confers upon experienced persons, but which they are

totally unable to communicate to others. When the mind of a mechanician is occupied with the contrivance of a machine, he must wait until, in the midst of his meditations, some happy combination presents itself to his mind which may answer his purpose.” Robert Willis, Principles of Mechanism [32]

Almost two centuries later, a rational method for the synthesis in many domains is still not clear. Though many best-practice design methodologies exist, at the end of the day they rely on elusive human creativity. Product design is still taught today largely through apprenticeship: Engineering students learn about existing solutions and techniques for well-defined, relatively simple problems, and then – through practice – are expected to improve and combine these to create larger, more complex systems. How is this synthesis process done? We do not know, but we cloak it with the term “creativity”. The question of how synthesis of complex systems occurs has been divided in a dichotomy of two views: One view is that complex systems emerge through successive adaptations coupled with natural selection. This Darwinian process is well accepted in Biology, but is more controversial in engineering [2,35]. The alternative explanation...

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