Locomotion of robotic and virtual agents is a
challenging task requiring the control of several degrees of
freedom as well as the coordination of multiple subsystems.
Traditionally, it is engineered by top-down design and finetuning
of the agent’s morphology and controller. A relatively
recent trend in fields such as evolutionary robotics, computer
animation and artificial life has been the coevolution and mutual
adaptation of the morphology and controller in computational
agent models. However, the controller is generally modeled as a
complex system, often a neural or gene regulatory network. In the
present study, inspired by molecular biology and based on normal
modal analysis, we formulate a behavior-finding framework for
the design of bipedal agents that are able to walk along a
filament and have no explicit control system. Instead, agents
interact with their environment in a purely reactive way. A simple
mutation operator, based on physical relaxation, is used to drive
the evolutionary search. Results show that gait patterns can be
evolutionarily engineered from the spatial interaction between
precisely tuned morphologies and the environment.
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