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

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