The Watchmaker's guide to Artificial Life: On the Role of Death, Modularity and Physicality in Evolutionary Robotics

Optimizing robots through the implementation of evolutionary processes
plays a key role in evolutionary robotics and articial life. Challenges that
arise in the evolutionary optimization of robots are usually related to an al-
gorithm's compromise between trying new solutions and improving previously
found solutions (the exploration vs. exploitation trade-o), and whether to
express genomic information directly or generatively (genotype to phenotype
mapping). An additional challenge is that there is a discrepancy between sim-
ulation and reality (reality gap) where robots 'evolved' in simulation environ-
ments function dierently when transferred to the real-world.
The exploration vs. exploitation trade-o is addressed through describing
and experimenting with the inclusion of biologically-inspired intrinsic mortality
and how this aects the evolvability of populations. The results contribute to
our understanding of the relationship between intrinsic mortality and mutation
rate. The results further indicate how it can be utilized to develop algorithms
that can outperform state-of-the-art algorithms.
This thesis continues by addressing the challenge of mapping the genotype
to the phenotype through investigating the in
uence of generative encodings
on the evolution of simulated modular robots. It is investigated how an L-
System as a generative encoding can lead to the formation of plant-inspired
virtual creatures and shows that movement for tracking a moving light source
is not an emerging phenotypic trait. Afterwards, this generative encoding is
shown to better evolve modular robots for locomotion compared to a direct
encoding. The addition of real-world solar panel modules demonstrates how
modular robots can be evolved toward energy autonomy.
The nal part of this thesis describes the evolution of embodiment and
control of physical robots. As part of this, an automated process for assem-
bly and disassembly of modular robots is demonstrated, which can be used
to evaluate evolved individuals in the real world. A viable method for imple-
menting evolution directly is demonstrated through evolving the behavior of a
knifesh-inspired physical soft robot. Both approaches represent strategies for
addressing the reality gap.
The experimental results of this thesis contribute to the understanding of
biological phenomena and elucidate how improvements can be made to existing
methods in evolutionary robotics. It shows that we can utilize concepts from
evolutionary biology to advance our understanding of evolutionary dynamics,
encodings and physical designs that are valuable for the automated design of
robots.