Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better
Jie Luo, Karine Miras, Jakub Tomczak, Agoston E. Eiben

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…
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Taxonomy
TopicsEvolutionary Algorithms and Applications · Reinforcement Learning in Robotics · Evolution and Genetic Dynamics
