Compact representations circumvent the computational obstacle to complexity.

A method of automated design of complex, modular robots involves an evolutionary process in which generative representations of designs are used. The term “generative representations” as used here signifies, loosely, representations that consist of or include algorithms, computer programs, and the like, wherein encoded designs can reuse elements of their encoding and thereby evolve toward greater complexity.

The “Quatrobot” Is a Walking Robot that was designed by automated evolutionary synthesis, using a generative representation. The robot was built after 13 iterations.

Generative representations are compact representations and were devised as means to circumvent the above-mentioned fundamental restriction on scalability. In the present method, a robot is defined by a compact programmatic form (its generative representation) and the evolutionary variation takes place on this form. The evolutionary process is an iterative one, wherein each cycle consists of the following steps:

1. Generative representations are generated in an evolutionary subprocess.
2. Each generative representation is a program that, when compiled, produces an assembly procedure.
3. In a computational simulation, a constructor executes an assembly procedure to generate a robot.
4. A physical-simulation program tests the performance of a simulated constructed robot, evaluating the performance according to a fitness criterion to yield a figure of merit that is fed back into the evolutionary subprocess of the next iteration.

In comparison with prior approaches to automated evolutionary design of robots, the use of generative representations offers two advantages: First, a generative representation enables the reuse of components in regular and hierarchical ways and thereby serves a systematic means of creating more complex modules out of simpler ones. Second, the evolved generative representation may capture intrinsic properties of the design problem, so that variations in the representations move through the design space more effectively than do equivalent variations in a nongenerative representation.

This method has been demonstrated by using it to design some robots that move, variously, by walking, rolling, or sliding. Some of the robots were built (see figure). Although these robots are very simple, in comparison with robots designed by humans, their structures are more regular, modular, hierarchical, and complex than are those of evolved designs of comparable functionality synthesized by use of nongenerative representations.

This work was done by Gregory S. Hornby of Ames Research Center, Hod Lipson of Cornell University, and Jordan B. Pollack of Brandeis University. For more information, download the Technical Support Package (free white paper) at www.techbriefs.com/tsp under the Information Sciences category.

Inquiries concerning rights for the commercial use of this invention should be addressed to the Technology Partnerships Division, Ames Research Center, (650) 604-2954. Refer to ARC-15334-1

This Brief includes a Technical Support Package (TSP).

Generative Representations for Automated Design of Robots (reference ARC-15334-1) is currently available for download from the TSP library.

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