Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better

TL;DR

This study explores how inheriting learned traits, inspired by Lamarckian evolution, can enhance robot evolution by improving the development of robot bodies and controllers through simulation comparisons.

Contribution

It introduces a simulation framework comparing Lamarckian and Darwinian evolution in robots, revealing how inheritance of learned traits accelerates evolution and morphological intelligence.

Findings

Lamarckian systems show faster evolution of robot controllers.

Inherited learned traits improve the match between robot bodies and brains.

Lamarckism increases the emergence of morphological intelligence.

Abstract

Evolutionary robot systems offer two principal advantages: an advanced way of developing robots through evolutionary optimization and a special research platform to conduct what-if experiments regarding questions about evolution. Our study sits at the intersection of these. We investigate the question ``What if the 18th-century biologist Lamarck was not completely wrong and individual traits learned during a lifetime could be passed on to offspring through inheritance?'' We research this issue through simulations with an evolutionary robot framework where morphologies (bodies) and controllers (brains) of robots are evolvable and robots also can improve their controllers through learning during their lifetime. Within this framework, we compare a Lamarckian system, where learned bits of the brain are inheritable, with a Darwinian system, where they are not. Analyzing simulations based on these systems, we obtain new insights about Lamarckian evolution dynamics and the interaction between evolution and learning. Specifically, we show that Lamarckism amplifies the emergence of `morphological intelligence', the ability of a given robot body to acquire a good brain by learning, and identify the source of this success: `newborn' robots have a higher fitness because their inherited brains match their bodies better than those in a Darwinian system.