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 journalArticlepeer-review

2 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 languageEnglish (US)
Article numbere2120019119
JournalProceedings of the National Academy of Sciences of the United States of America
Volume119
Issue number12
DOIs
StatePublished - Mar 22 2022

All Science Journal Classification (ASJC) codes

  • General

Keywords

  • Adaptable landscapes
  • Evolution
  • Robotic biology
  • Stochastic dynamics

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