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 ( josh.bongard@uvm.edu) 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.
copyright held by owners/author(s).