Online adaptation in robots as biological development provides phenotypic plasticity

TL;DR

This paper demonstrates that robots with Boolean network controllers can achieve environmental adaptation through online sensor coupling adjustments, exhibiting phenotypic plasticity akin to biological development, especially in critical dynamical regimes.

Contribution

It introduces a method for robotic phenotypic plasticity via online sensor adaptation without altering the network structure, highlighting the importance of critical dynamics for optimal performance.

Findings

Robots can adapt navigation and collision avoidance by adjusting sensor coupling.

Critical dynamical regimes enable the best balance of robustness and adaptability.

Phenotypic plasticity in robots parallels biological development mechanisms.

Abstract

The ability of responding to environmental stimuli with appropriate actions is a property shared by all living organisms, and it is also sought in the design of robotic systems. Phenotypic plasticity provides a way for achieving this property as it characterises those organisms that, from one genotype, can express different phenotypes in response to different environments, without involving genetic modifications. In this work we study phenotypic plasticity in robots that are equipped with online sensor adaptation. We show that Boolean network controlled robots can attain navigation with collision avoidance by adapting the coupling between proximity sensors and their controlling network without changing its structure. In other terms, these robots, while being characterised by one genotype (i.e. the network) can express a phenotype among many that is suited for the specific environment. We also show that the dynamical regime that makes it possible to attain the best overall performance is the critical one, bringing further evidence to the hypothesis that natural and artificial systems capable of optimally balancing robustness and adaptivity are critical.