  To maintain full autonomy, autonomous robotic systems must have the ability
to self-repair. Self-repairing via compensatory mechanisms appears in nature:
for example, some fish can lose even 76% of their propulsive surface without
loss of thrust by altering stroke mechanics. However, direct transference of
these alterations from an organism to a robotic flapping propulsor may not be
optimal due to irrelevant evolutionary pressures. We instead seek to determine
what alterations to stroke mechanics are optimal for a damaged robotic system
via artificial evolution. To determine whether natural and machine-learned
optima differ, we employ a cyber-physical system using a Covariance Matrix
Adaptation Evolutionary Strategy to seek the most efficient trajectory for a
given force. We implement an online optimization with hardware-in-the-loop,
performing experimental function evaluations with an actuated flexible flat
plate. To recoup thrust production following partial amputation, the most
efficient learned strategy was to increase amplitude, increase frequency,
increase the amplitude of angle of attack, and phase shift the angle of attack
by approximately 110 degrees. In fish, only an amplitude increase is reported
by majority in the literature. To recoup side-force production, a more
challenging optimization landscape is encountered. Nesting of optimal angle of
attack traces is found in the resultant-based reference frame, but no clear
trend in amplitude or frequency are exhibited -- in contrast to the increase in
frequency reported in insect literature. These results suggest that how
mechanical flapping propulsors most efficiently adjust to damage of a flapping
propulsor may not align with natural swimmers and flyers.
