Reweighting Rewards in Embodied Evolution to Achieve a Balanced Distribution of Labour

Embodied evolution aims at self-sufficient adaptation in robot collectives in their task environment.
An open question is how to achieve a good balance of effort over multiple tasks using embodied evolution.
Most efforts to date rely on switching between predefined behaviours or on spatial or temporal separation of the tasks to achieve this.
The research presented here is part of an effort to enable embodied evolutionary systems to achieve a balanced distribution of effort over multiple tasks without predefined behaviour and without any separation of the tasks.
We propose and experimentally evaluate a selection mechanism that introduces a local reproductive advantage to individuals that specialise in under-represented tasks.
The paper shows that an embodied evolution implementation with this mechanism leads to balanced populations of generalist individuals, even when the environment severely penalises generalist behaviour.
An extension that combines task-based and purely environmental selection in some cases leads to a balanced population of specialists, but does so inconsistently.