Robots as models of evolving systems

Gao Wang; Trung V. Phan; Shengkai Li; Jing Wang; Yan Peng; Guo Chen; Junle Qu; Daniel I. Goldman; Simon A. Levin; Kenneth Pienta; Sarah Amend; Robert H. Austin; Liyu Liu

doi:10.1073/pnas.2120019119

Robots as models of evolving systems

Gao Wang, Trung V. Phan, Shengkai Li, Jing Wang, Yan Peng, Guo Chen, Junle Qu, Daniel I. Goldman, Simon A. Levin, Kenneth Pienta, Sarah Amend, Robert H. Austin, Liyu Liu

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, and breeding. We map the quasi-steady-state surviving local density of the robots onto a multidimensional abstract “survival landscape.” We show that robot death in complex, self-adaptive stress landscapes proceeds by a general lowering of the robotic genetic diversity, and that stochastically changing landscapes are the most difficult to survive.

Original language	English (US)
Article number	e2120019119
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	119
Issue number	12
DOIs	https://doi.org/10.1073/pnas.2120019119
State	Published - Mar 22 2022

All Science Journal Classification (ASJC) codes

General

Keywords

Adaptable landscapes
Evolution
Robotic biology
Stochastic dynamics

Access to Document

10.1073/pnas.2120019119

Cite this

@article{81330f5447894da48bf1fc576e39bd3c,

title = "Robots as models of evolving systems",

abstract = "Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, and breeding. We map the quasi-steady-state surviving local density of the robots onto a multidimensional abstract “survival landscape.” We show that robot death in complex, self-adaptive stress landscapes proceeds by a general lowering of the robotic genetic diversity, and that stochastically changing landscapes are the most difficult to survive.",

keywords = "Adaptable landscapes, Evolution, Robotic biology, Stochastic dynamics",

author = "Gao Wang and Phan, {Trung V.} and Shengkai Li and Jing Wang and Yan Peng and Guo Chen and Junle Qu and Goldman, {Daniel I.} and Levin, {Simon A.} and Kenneth Pienta and Sarah Amend and Austin, {Robert H.} and Liyu Liu",

note = "Publisher Copyright: Copyright {\textcopyright} 2022 the Author(s).",

year = "2022",

month = mar,

day = "22",

doi = "10.1073/pnas.2120019119",

language = "English (US)",

volume = "119",

journal = "Proceedings of the National Academy of Sciences of the United States of America",

issn = "0027-8424",

publisher = "National Academy of Sciences",

number = "12",

}

TY - JOUR

T1 - Robots as models of evolving systems

AU - Wang, Gao

AU - Phan, Trung V.

AU - Li, Shengkai

AU - Wang, Jing

AU - Peng, Yan

AU - Chen, Guo

AU - Qu, Junle

AU - Goldman, Daniel I.

AU - Levin, Simon A.

AU - Pienta, Kenneth

AU - Amend, Sarah

AU - Austin, Robert H.

AU - Liu, Liyu

PY - 2022/3/22

Y1 - 2022/3/22

N2 - Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, and breeding. We map the quasi-steady-state surviving local density of the robots onto a multidimensional abstract “survival landscape.” We show that robot death in complex, self-adaptive stress landscapes proceeds by a general lowering of the robotic genetic diversity, and that stochastically changing landscapes are the most difficult to survive.

AB - Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, and breeding. We map the quasi-steady-state surviving local density of the robots onto a multidimensional abstract “survival landscape.” We show that robot death in complex, self-adaptive stress landscapes proceeds by a general lowering of the robotic genetic diversity, and that stochastically changing landscapes are the most difficult to survive.

KW - Adaptable landscapes

KW - Evolution

KW - Robotic biology

KW - Stochastic dynamics

UR - http://www.scopus.com/inward/record.url?scp=85126690264&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85126690264&partnerID=8YFLogxK

U2 - 10.1073/pnas.2120019119

DO - 10.1073/pnas.2120019119

M3 - Article

C2 - 35298335

AN - SCOPUS:85126690264

SN - 0027-8424

VL - 119

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

IS - 12

M1 - e2120019119

ER -

Robots as models of evolving systems

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this