it differs from previously generated
solutions. This approach was found to
produce walking in simulated bipedal
robots, a notoriously difficult problem
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.
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
1. auerbach, J.e. and bongard, J.c. on the relationship
between environmental and morphological complexity
in evolved robots. In Proceedings of the 2012 Genetic
and Evolutionary Computation Conference, 521–528.
2. beer, r.D. the dynamics of brain-body-environment
systems: a status report. Handbook of Cognitive
Science: An Embodied Approach (2008), 99–120.
3. bongard, J. morphological change in machines
accelerates the evolution of robust behavior. In
Proceedings of the National Academy of Sciences 108,
4 (2011), 1234.
4. bongard, J. zykov, V. and lipson, h. resilient machines
through continuous self-modeling. Science 314 (2006),
5. cheney, n., maccurdy, r., clune, J. and lipson, h.
unshackling evolution: evolving soft robots with
multiple materials and a powerful generative
encoding. In Proceedings of the Genetic and
Evolutionary Computation Conference. acm, ny, 2013.
6. clune, J., beckmann, b.e., ofria, c. and r.t. Pennock,
r.t. evolving coordinated quadruped gaits with the
hyperneat generative encoding. IEEE Congress on
Evolutionary Computation (2009), 2764–2771.
7. collins, S., ruina, a., tedrake, r. and Wisse, m. efficient
bipedal robots based on passive-dynamic walkers.
Science 307, 5712 (2005), 1082–1085.
8. edlund, J.a., chaumont, n., hintze, a., koch, c., tononi,
g. and adami, c. Integrated information increases
with fitness in the evolution of animats. PLoS
Computational Biology 7, 10 (2011).
9. floreano, D. and mattiussi, c. Bio-Inspired Artificial
Intelligence: Theories, Methods, and Technologies.
mIt Press, cambridge, ma, 2008.
10. frutiger, D.r., bongard, J.c. and Iida, f. Iterative
product engineering: evolutionary robot design. In
Proceedings of the Fifth International Conference
on Climbing and Walking Robots. P. bidaud and f.b.
amar, eds. Professional engineering Publishing, 2002,
11. hauert, S., zufferey, J.c. and floreano, D. evolved
swarming without positioning information:
an application in aerial communication relay.
Autonomous Robotics 26 (2009), 21–32.
12. hornby, g.S. and Pollack, J.b. creating high-level
components with a generative representation for body-brain evolution. Artificial Life 8, 3 (2002), 223–246.
13. Iida, f. and laschi, c. Soft robotics: challenges and
perspectives. Procedia Computer Science 7 (2011),
14. Izquierdo, e. and buhrmann, t. analysis of a
dynamical recurrent neural network evolved for
two qualitatively different tasks: Walking and
chemotaxis. Artificial Life XI: Proceedings of the
11th International Conference on the Simulation and
Synthesis of Living Systems. mIt Press, cambridge,
ma, 2008, 257–264.
15. Jakobi, n., husbands, P. and harvey, I. noise and the
reality gap: the use of simulation in evolutionary
robotics. Advances in Artificial Life (1995), 704–720.
16. koos, S., mouret, J.-m. and S. Doncieux, S. the
transferability approach: crossing the reality gap
in evolutionary robotics. IEEE Transactions on
Evolutionary Computation (2012); doi: 10.1109/
17. lehman, J. and Stanley, k.o. abandoning objectives:
evolution through the search for novelty alone.
Evolutionary Computation 19, 2 (2011), 189–223.
18. lipson, h. and Pollack, J.b. automatic design and
manufacture of artificial lifeforms. Nature 406 (2000),
19. long, J. Darwin’s Devices: What Evolving Robots Can
Teach Us about the History of Life and the Future of
Technology. basic books, 2012.
20. luke, S. and Spector, l. evolving teamwork and
coordination with genetic programming. In
Proceedings of the First Annual Conference on
Genetic Programming. mIt Press, cambridge, ma,
21. lungarella, m., metta, g., Pfeifer, r. and Sandini, g.
Developmental robotics: a survey. Connection Science
15, 4 (2003), 151–190.
22. mataric, m. and cliff, D. challenges in evolving
controllers for physical robots. Robotics and
Autonomous Systems 19 (1996), 67–84.
Josh C. Bongard ( email@example.com) 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).