  This research considers the task of evolving the physical structure of a
robot to enhance its performance in various environments, which is a
significant problem in the field of Evolutionary Robotics. Inspired by the
fields of evolutionary art and sculpture, we evolve only targeted parts of a
robot, which simplifies the optimisation problem compared to traditional
approaches that must simultaneously evolve both (actuated) body and brain.
Exploration fidelity is emphasised in areas of the robot most likely to benefit
from shape optimisation, whilst exploiting existing robot structure and
control. Our approach uses a Genetic Algorithm to optimise collections of
Bezier splines that together define the shape of a legged robot's tibia, and
leg performance is evaluated in parallel in a high-fidelity simulator. The leg
is represented in the simulator as 3D-printable file, and as such can be
readily instantiated in reality. Provisional experiments in three distinct
environments show the evolution of environment-specific leg structures that are
both high-performing and notably different to those evolved in the other
environments. This proof-of-concept represents an important step towards the
environment-dependent optimisation of performance-critical components for a
range of ubiquitous, standard, and already-capable robots that can carry out a
wide variety of tasks.
