Evolutionary Robotics

Evolutionary robotics is a sub-field of artificial life in which evolutionary algorithms are used to evolve robot morphology (i.e. shape) and/or behavior . Sometimes these robots exist only in simulation, while other times evolved morphologies and behaviors are translated out of the computer and into physical robots. Getting robots evolved in a computer to work in the real world is notoriously challenging due to what is known as the “reality gap” (i.e. the many unexpected differences between a simulated environment and reality), but there has increasingly been progress towards overcoming this problem.

For a video introduction to evolutionary robotics, see the following talk by Sam Kriegman at the 2021 ISAL Summer school:

History

The first well-known instance of evolutionary robotics was the work of Karl Sims, who evolved life-like moving creatures in simulation out of rigid block-like components :

A few years later, Hod Lipson and Jordan Pollack used the same principles to evolve robots that could be physically constructed in real life :

Since then, the field has flourished and continued in a variety of directions.

Robot Locomotion

One central challenge in evolutionary robotics is how to evolve effective locomotion behaviors (or gaits) for a given robot morphology. This problem is often tackled from an evolutionary computation perspective. Locomotion involves a variety of sub-problems:

Wall-crossing

Jared Moore and Adam Stanton have carried out a detailed set of investigations on a simulated wall-crossing task, for which they have found lexicase selection to be a particularly effective strategy .

Injury recovery

Another common task in robot locomotion is evolving robots that are capable of adapting their gait when they are damaged. One recent prominent solution to this problem used the MAP-Elites algorithm to evolve a suite of gaits for a hexapod robot that used different legs to different extents .

Complex robot behaviors

There has also been a substantial amount of work on evolving more complex high-level of behavioral controllers for robots. One particularly notable example of this was the Twitch Plays Robotics project, in which a crowd of human observers functioned as the fitness function for evaluating simulated robots’ performances on a range of behavioral tasks .

Navigation

As a task that most robots need to be able to complete, navigation is a commonly-studied higher level behavior. For example, Ken Stanley and colleagues have often used maze-solving tasks for simulated robots as an illustrative example problem . Navigation in a physical robot has also been used as proof-of-concept task for demonstrating that algorithms evolved in Avida can successfully be translated to physical robots .

Soft-bodied robots

Soft-bodied robots are a particularly popular substrate for research on the evolution of robot morphology . These robots are traditionally evolved in a computer as a cube composed of voxels made up of various different materials with different properties. Recently, this approach has been translated to a non-simulated context via the xenobots project .

References

Brant, J. C., & Stanley, K. O. (2017). Minimal criterion coevolution: a new approach to open-ended search. 67–74. https://doi.org/10/ggcgzb
Moore, J. M., & Stanton, A. (2021). Objective Sampling Strategies for Generalized Locomotion Behavior with Lexicase Selection. ALIFE 2021: The 2021 Conference on Artificial Life. https://doi.org/10/gk86ct
Matthews, D., & Bongard, J. (2020). Crowd grounding: finding semantic and behavioral alignment through human robot interaction. The 2020 Conference on Artificial Life, 148–156. https://doi.org/10/gncq6n
Bryson, D. M., Wagner, A. P., & Ofria, C. (2014). There and back again: gene-processing hardware for the evolution and robotic deployment of robust navigation strategies. 689–696. https://doi.org/10/gncq6j
Moore, J. M., & Stanton, A. (2018). Tiebreaks and Diversity: Isolating Effects in Lexicase Selection. 590–597. https://doi.org/10/ffm3
Moore, J. M., & Stanton, A. (2017). Lexicase selection outperforms previous strategies for incremental evolution of virtual creature controllers. 290–297. https://doi.org/10.1162/isal_a_050
Moore, J. M., & Stanton, A. (2020). When Specialists Transition to Generalists: Evolutionary Pressure in Lexicase Selection. 719–726. https://doi.org/10/ffm4
Moore, J. M., & Stanton, A. (2019). The Limits of Lexicase Selection in an Evolutionary Robotics Task. 551–558. https://doi.org/10/fdz9
Moore, J. M., & Clark, A. J. (2021, July 19). Supervision and Evolution: Pretraining Neural Networks for Quadrupedal Locomotion. ALIFE 2021: The 2021 Conference on Artificial Life. https://doi.org/10/gk86b8
Cheney, N., MacCurdy, R., Clune, J., & Lipson, H. (2014). Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding. ACM SIGEVOlution, 7(1), 11–23. https://doi.org/10/ggdr7m
Cully, A., Clune, J., Tarapore, D., & Mouret, J.-B. (2015). Robots that can adapt like animals. Nature, 521(7553), 503–507. https://doi.org/10/gdg46t
Kriegman, S., Blackiston, D., Levin, M., & Bongard, J. (2020). A scalable pipeline for designing reconfigurable organisms. Proceedings of the National Academy of Sciences, 117(4), 1853–1859. https://doi.org/10/gghxd3
Lipson, H., & Pollack, J. B. (2000). Automatic design and manufacture of robotic lifeforms. Nature, 406(6799), 974–978. https://doi.org/10/c62vc2
Sims, K. (1994). Evolving 3D Morphology and Behavior by Competition. Artificial Life, 1(4), 353–372. https://doi.org/10/czj6hc
Bongard, J. C. (2013). Evolutionary robotics. Communications of the ACM, 56(8), 74–83. https://doi.org/10/gkdf3m
Tags: ,