it differs from previously generated solutions. This approach was found to produce walking in simulated bipedal robots, a notoriously difficult problem in robotics.

conclusion

Since its founding in the early 1990s, evolutionary robotics has remained a small but productive niche field. Although the field has yet to evolve a robot that is superior to one produced using mainstream optimization methods such as reinforcement learning, the field has produced a wider variety of robots automatically. Depending on how one counts, roboticists have manually designed and built a few hundred different kinds of robots with humanoid or legged or snakelike body plans. Evolutionary methods, on the other hand have produced millions of different kinds of robots that can walk (for example, Figure 1a–d), swim, or grasp objects. 33 It is hoped that by exploring all the different ways that robots achieve these basic competencies we might gain unique insight into how to scale robots up to perform more complex tasks, like working safely alongside a human.

Moreover, several recent advances in fields outside of robotics are providing opportunities to showcase the advantages of this evolutionary approach. Advances in materials science are making soft robots and modular robots a reality, yet manually designing and controlling such robots is much less intuitive than traditional rigid and monolithic robots. Advances in automated fabrication are bringing the possibility of continuous and automated design, manufacture, and deployment of robots within reach. State-of-the-art evolutionary algorithms and physical simulators are making it possible to optimize all aspects of a robot’s body plan and control policy simultaneously in a reasonable time period. And finally, new insights from evolutionary biology and neuroscience are informing our ability to create increasingly complex, autonomous, and adaptive machines.

acknowledgments

This work was supported by the National
Science Foundation (NSF) under grant
PECASE-0953837, and by the Defense
Advanced Research Projects Agency
(DARPA) under grants W911NF-11-1-0076
and FA8650-11-1-7155.

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Josh C. Bongard ( [e-mail]) is director of the morphology, evolution and cognition laboratory in the Department of computer Science at the university of Vermont. he is also a member of the Vermont advanced computing core and the complex Systems center.

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