Toward Population-Level Biohybrid Systems: Bioinspiration and Behavior

For biohybrid systems involving robot interactions with a large and varied population of animals, it could be beneficial to deploy a morphologically and behaviorally varied population of robots into the environment, for successful interactions across the full diversity of the relevant biological population. In this paper, we briefly summarize our work in two areas integral to this effort: (1) computational investigations of bioinspired methods for retaining population-level variance under evolution; and (2) quantitative evolutionary analysis of genetics and morphology. We also consider ideas for Cognitive Science-inspired work in designing goal-directed behaviors for the robots in biohybrid systems. Based on the underlying idea that robot designs with deeper roots in biology can result in more effective biohybrid systems, our perspectives and approaches could illuminate new commonalities between evolved robot populations and evolved biological populations, which would ideally improve the robots as tools for scientific insight into animals, their behaviors, and their environments.